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Commit 75d93027 authored by bailuo's avatar bailuo
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lib/test/evaluation/itbdataset.py 0 → 100644
View file @ 75d93027
import numpy as np
from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList
from lib.test.utils.load_text import load_text
import os
class ITBDataset(BaseDataset):
""" NUS-PRO dataset
"""
def __init__(self):
super().__init__()
self.base_path = self.env_settings.itb_path
self.sequence_info_list = self._get_sequence_info_list(self.base_path)
def get_sequence_list(self):
return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list])
def _construct_sequence(self, sequence_info):
sequence_path = sequence_info['path']
nz = sequence_info['nz']
ext = sequence_info['ext']
start_frame = sequence_info['startFrame']
end_frame = sequence_info['endFrame']
init_omit = 0
if 'initOmit' in sequence_info:
init_omit = sequence_info['initOmit']
frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path,
sequence_path=sequence_path, frame=frame_num,
nz=nz, ext=ext) for frame_num in
range(start_frame + init_omit, end_frame + 1)]
anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path'])
# NOTE: NUS has some weird annos which panda cannot handle
ground_truth_rect = load_text(str(anno_path), delimiter=(',', None), dtype=np.float64, backend='numpy')
return Sequence(sequence_info['name'], frames, 'otb', ground_truth_rect[init_omit:, :],
object_class=sequence_info['object_class'])
def __len__(self):
return len(self.sequence_info_list)
def get_fileNames(self, rootdir):
fs = []
fs_all = []
for root, dirs, files in os.walk(rootdir, topdown=True):
files.sort()
files.sort(key=len)
if files is not None:
for name in files:
_, ending = os.path.splitext(name)
if ending == ".jpg":
_, root_ = os.path.split(root)
fs.append(os.path.join(root_, name))
fs_all.append(os.path.join(root, name))
return fs_all, fs
def _get_sequence_info_list(self, base_path):
sequence_info_list = []
for scene in os.listdir(base_path):
if '.' in scene:
continue
videos = os.listdir(os.path.join(base_path, scene))
for video in videos:
_, fs = self.get_fileNames(os.path.join(base_path, scene, video))
video_tmp = {"name": video, "path": scene + '/' + video, "startFrame": 1, "endFrame": len(fs),
"nz": len(fs[0].split('/')[-1].split('.')[0]), "ext": "jpg",
"anno_path": scene + '/' + video + "/groundtruth.txt",
"object_class": "unknown"}
sequence_info_list.append(video_tmp)
return sequence_info_list # sequence_info_list_50 #
lib/test/evaluation/lasot_lmdbdataset.py 0 → 100644
View file @ 75d93027
from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList
from lib.utils.lmdb_utils import *
'''2021.1.27 LaSOT dataset using lmdb data'''
class LaSOTlmdbDataset(BaseDataset):
"""
LaSOT test set consisting of 280 videos (see Protocol-II in the LaSOT paper)
Publication:
LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking
Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao and Haibin Ling
CVPR, 2019
https://arxiv.org/pdf/1809.07845.pdf
Download the dataset from https://cis.temple.edu/lasot/download.html
"""
def __init__(self):
super().__init__()
self.base_path = self.env_settings.lasot_lmdb_path
self.sequence_list = self._get_sequence_list()
self.clean_list = self.clean_seq_list()
def clean_seq_list(self):
clean_lst = []
for i in range(len(self.sequence_list)):
cls, _ = self.sequence_list[i].split('-')
clean_lst.append(cls)
return clean_lst
def get_sequence_list(self):
return SequenceList([self._construct_sequence(s) for s in self.sequence_list])
def _construct_sequence(self, sequence_name):
class_name = sequence_name.split('-')[0]
anno_path = str('{}/{}/groundtruth.txt'.format(class_name, sequence_name))
# decode the groundtruth
gt_str_list = decode_str(self.base_path, anno_path).split('\n')[:-1] # the last line is empty
gt_list = [list(map(float, line.split(','))) for line in gt_str_list]
ground_truth_rect = np.array(gt_list).astype(np.float64)
# decode occlusion file
occlusion_label_path = str('{}/{}/full_occlusion.txt'.format(class_name, sequence_name))
occ_list = list(map(int, decode_str(self.base_path, occlusion_label_path).split(',')))
full_occlusion = np.array(occ_list).astype(np.float64)
# decode out of view file
out_of_view_label_path = str('{}/{}/out_of_view.txt'.format(class_name, sequence_name))
out_of_view_list = list(map(int, decode_str(self.base_path, out_of_view_label_path).split(',')))
out_of_view = np.array(out_of_view_list).astype(np.float64)
target_visible = np.logical_and(full_occlusion == 0, out_of_view == 0)
frames_path = '{}/{}/img'.format(class_name, sequence_name)
frames_list = [[self.base_path, '{}/{:08d}.jpg'.format(frames_path, frame_number)] for frame_number in range(1, ground_truth_rect.shape[0] + 1)]
target_class = class_name
return Sequence(sequence_name, frames_list, 'lasot', ground_truth_rect.reshape(-1, 4),
object_class=target_class, target_visible=target_visible)
def __len__(self):
return len(self.sequence_list)
def _get_sequence_list(self):
sequence_list = ['airplane-1',
'airplane-9',
'airplane-13',
'airplane-15',
'basketball-1',
'basketball-6',
'basketball-7',
'basketball-11',
'bear-2',
'bear-4',
'bear-6',
'bear-17',
'bicycle-2',
'bicycle-7',
'bicycle-9',
'bicycle-18',
'bird-2',
'bird-3',
'bird-15',
'bird-17',
'boat-3',
'boat-4',
'boat-12',
'boat-17',
'book-3',
'book-10',
'book-11',
'book-19',
'bottle-1',
'bottle-12',
'bottle-14',
'bottle-18',
'bus-2',
'bus-5',
'bus-17',
'bus-19',
'car-2',
'car-6',
'car-9',
'car-17',
'cat-1',
'cat-3',
'cat-18',
'cat-20',
'cattle-2',
'cattle-7',
'cattle-12',
'cattle-13',
'spider-14',
'spider-16',
'spider-18',
'spider-20',
'coin-3',
'coin-6',
'coin-7',
'coin-18',
'crab-3',
'crab-6',
'crab-12',
'crab-18',
'surfboard-12',
'surfboard-4',
'surfboard-5',
'surfboard-8',
'cup-1',
'cup-4',
'cup-7',
'cup-17',
'deer-4',
'deer-8',
'deer-10',
'deer-14',
'dog-1',
'dog-7',
'dog-15',
'dog-19',
'guitar-3',
'guitar-8',
'guitar-10',
'guitar-16',
'person-1',
'person-5',
'person-10',
'person-12',
'pig-2',
'pig-10',
'pig-13',
'pig-18',
'rubicCube-1',
'rubicCube-6',
'rubicCube-14',
'rubicCube-19',
'swing-10',
'swing-14',
'swing-17',
'swing-20',
'drone-13',
'drone-15',
'drone-2',
'drone-7',
'pool-12',
'pool-15',
'pool-3',
'pool-7',
'rabbit-10',
'rabbit-13',
'rabbit-17',
'rabbit-19',
'racing-10',
'racing-15',
'racing-16',
'racing-20',
'robot-1',
'robot-19',
'robot-5',
'robot-8',
'sepia-13',
'sepia-16',
'sepia-6',
'sepia-8',
'sheep-3',
'sheep-5',
'sheep-7',
'sheep-9',
'skateboard-16',
'skateboard-19',
'skateboard-3',
'skateboard-8',
'tank-14',
'tank-16',
'tank-6',
'tank-9',
'tiger-12',
'tiger-18',
'tiger-4',
'tiger-6',
'train-1',
'train-11',
'train-20',
'train-7',
'truck-16',
'truck-3',
'truck-6',
'truck-7',
'turtle-16',
'turtle-5',
'turtle-8',
'turtle-9',
'umbrella-17',
'umbrella-19',
'umbrella-2',
'umbrella-9',
'yoyo-15',
'yoyo-17',
'yoyo-19',
'yoyo-7',
'zebra-10',
'zebra-14',
'zebra-16',
'zebra-17',
'elephant-1',
'elephant-12',
'elephant-16',
'elephant-18',
'goldfish-3',
'goldfish-7',
'goldfish-8',
'goldfish-10',
'hat-1',
'hat-2',
'hat-5',
'hat-18',
'kite-4',
'kite-6',
'kite-10',
'kite-15',
'motorcycle-1',
'motorcycle-3',
'motorcycle-9',
'motorcycle-18',
'mouse-1',
'mouse-8',
'mouse-9',
'mouse-17',
'flag-3',
'flag-9',
'flag-5',
'flag-2',
'frog-3',
'frog-4',
'frog-20',
'frog-9',
'gametarget-1',
'gametarget-2',
'gametarget-7',
'gametarget-13',
'hand-2',
'hand-3',
'hand-9',
'hand-16',
'helmet-5',
'helmet-11',
'helmet-19',
'helmet-13',
'licenseplate-6',
'licenseplate-12',
'licenseplate-13',
'licenseplate-15',
'electricfan-1',
'electricfan-10',
'electricfan-18',
'electricfan-20',
'chameleon-3',
'chameleon-6',
'chameleon-11',
'chameleon-20',
'crocodile-3',
'crocodile-4',
'crocodile-10',
'crocodile-14',
'gecko-1',
'gecko-5',
'gecko-16',
'gecko-19',
'fox-2',
'fox-3',
'fox-5',
'fox-20',
'giraffe-2',
'giraffe-10',
'giraffe-13',
'giraffe-15',
'gorilla-4',
'gorilla-6',
'gorilla-9',
'gorilla-13',
'hippo-1',
'hippo-7',
'hippo-9',
'hippo-20',
'horse-1',
'horse-4',
'horse-12',
'horse-15',
'kangaroo-2',
'kangaroo-5',
'kangaroo-11',
'kangaroo-14',
'leopard-1',
'leopard-7',
'leopard-16',
'leopard-20',
'lion-1',
'lion-5',
'lion-12',
'lion-20',
'lizard-1',
'lizard-3',
'lizard-6',
'lizard-13',
'microphone-2',
'microphone-6',
'microphone-14',
'microphone-16',
'monkey-3',
'monkey-4',
'monkey-9',
'monkey-17',
'shark-2',
'shark-3',
'shark-5',
'shark-6',
'squirrel-8',
'squirrel-11',
'squirrel-13',
'squirrel-19',
'volleyball-1',
'volleyball-13',
'volleyball-18',
'volleyball-19']
return sequence_list
lib/test/evaluation/lasotdataset.py 0 → 100644
View file @ 75d93027
import numpy as np
from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList
from lib.test.utils.load_text import load_text
class LaSOTDataset(BaseDataset):
"""
LaSOT test set consisting of 280 videos (see Protocol-II in the LaSOT paper)
Publication:
LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking
Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao and Haibin Ling
CVPR, 2019
https://arxiv.org/pdf/1809.07845.pdf
Download the dataset from https://cis.temple.edu/lasot/download.html
"""
def __init__(self):
super().__init__()
self.base_path = self.env_settings.lasot_path
self.sequence_list = self._get_sequence_list()
self.clean_list = self.clean_seq_list()
def clean_seq_list(self):
clean_lst = []
for i in range(len(self.sequence_list)):
cls, _ = self.sequence_list[i].split('-')
clean_lst.append(cls)
return clean_lst
def get_sequence_list(self):
return SequenceList([self._construct_sequence(s) for s in self.sequence_list])
def _construct_sequence(self, sequence_name):
class_name = sequence_name.split('-')[0]
anno_path = '{}/{}/{}/groundtruth.txt'.format(self.base_path, class_name, sequence_name)
ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64)
occlusion_label_path = '{}/{}/{}/full_occlusion.txt'.format(self.base_path, class_name, sequence_name)
# NOTE: pandas backed seems super super slow for loading occlusion/oov masks
full_occlusion = load_text(str(occlusion_label_path), delimiter=',', dtype=np.float64, backend='numpy')
out_of_view_label_path = '{}/{}/{}/out_of_view.txt'.format(self.base_path, class_name, sequence_name)
out_of_view = load_text(str(out_of_view_label_path), delimiter=',', dtype=np.float64, backend='numpy')
target_visible = np.logical_and(full_occlusion == 0, out_of_view == 0)
frames_path = '{}/{}/{}/img'.format(self.base_path, class_name, sequence_name)
frames_list = ['{}/{:08d}.jpg'.format(frames_path, frame_number) for frame_number in range(1, ground_truth_rect.shape[0] + 1)]
target_class = class_name
return Sequence(sequence_name, frames_list, 'lasot', ground_truth_rect.reshape(-1, 4),
object_class=target_class, target_visible=target_visible)
def __len__(self):
return len(self.sequence_list)
def _get_sequence_list(self):
sequence_list = ['airplane-1',
'airplane-9',
'airplane-13',
'airplane-15',
'basketball-1',
'basketball-6',
'basketball-7',
'basketball-11',
'bear-2',
'bear-4',
'bear-6',
'bear-17',
'bicycle-2',
'bicycle-7',
'bicycle-9',
'bicycle-18',
'bird-2',
'bird-3',
'bird-15',
'bird-17',
'boat-3',
'boat-4',
'boat-12',
'boat-17',
'book-3',
'book-10',
'book-11',
'book-19',
'bottle-1',
'bottle-12',
'bottle-14',
'bottle-18',
'bus-2',
'bus-5',
'bus-17',
'bus-19',
'car-2',
'car-6',
'car-9',
'car-17',
'cat-1',
'cat-3',
'cat-18',
'cat-20',
'cattle-2',
'cattle-7',
'cattle-12',
'cattle-13',
'spider-14',
'spider-16',
'spider-18',
'spider-20',
'coin-3',
'coin-6',
'coin-7',
'coin-18',
'crab-3',
'crab-6',
'crab-12',
'crab-18',
'surfboard-12',
'surfboard-4',
'surfboard-5',
'surfboard-8',
'cup-1',
'cup-4',
'cup-7',
'cup-17',
'deer-4',
'deer-8',
'deer-10',
'deer-14',
'dog-1',
'dog-7',
'dog-15',
'dog-19',
'guitar-3',
'guitar-8',
'guitar-10',
'guitar-16',
'person-1',
'person-5',
'person-10',
'person-12',
'pig-2',
'pig-10',
'pig-13',
'pig-18',
'rubicCube-1',
'rubicCube-6',
'rubicCube-14',
'rubicCube-19',
'swing-10',
'swing-14',
'swing-17',
'swing-20',
'drone-13',
'drone-15',
'drone-2',
'drone-7',
'pool-12',
'pool-15',
'pool-3',
'pool-7',
'rabbit-10',
'rabbit-13',
'rabbit-17',
'rabbit-19',
'racing-10',
'racing-15',
'racing-16',
'racing-20',
'robot-1',
'robot-19',
'robot-5',
'robot-8',
'sepia-13',
'sepia-16',
'sepia-6',
'sepia-8',
'sheep-3',
'sheep-5',
'sheep-7',
'sheep-9',
'skateboard-16',
'skateboard-19',
'skateboard-3',
'skateboard-8',
'tank-14',
'tank-16',
'tank-6',
'tank-9',
'tiger-12',
'tiger-18',
'tiger-4',
'tiger-6',
'train-1',
'train-11',
'train-20',
'train-7',
'truck-16',
'truck-3',
'truck-6',
'truck-7',
'turtle-16',
'turtle-5',
'turtle-8',
'turtle-9',
'umbrella-17',
'umbrella-19',
'umbrella-2',
'umbrella-9',
'yoyo-15',
'yoyo-17',
'yoyo-19',
'yoyo-7',
'zebra-10',
'zebra-14',
'zebra-16',
'zebra-17',
'elephant-1',
'elephant-12',
'elephant-16',
'elephant-18',
'goldfish-3',
'goldfish-7',
'goldfish-8',
'goldfish-10',
'hat-1',
'hat-2',
'hat-5',
'hat-18',
'kite-4',
'kite-6',
'kite-10',
'kite-15',
'motorcycle-1',
'motorcycle-3',
'motorcycle-9',
'motorcycle-18',
'mouse-1',
'mouse-8',
'mouse-9',
'mouse-17',
'flag-3',
'flag-9',
'flag-5',
'flag-2',
'frog-3',
'frog-4',
'frog-20',
'frog-9',
'gametarget-1',
'gametarget-2',
'gametarget-7',
'gametarget-13',
'hand-2',
'hand-3',
'hand-9',
'hand-16',
'helmet-5',
'helmet-11',
'helmet-19',
'helmet-13',
'licenseplate-6',
'licenseplate-12',
'licenseplate-13',
'licenseplate-15',
'electricfan-1',
'electricfan-10',
'electricfan-18',
'electricfan-20',
'chameleon-3',
'chameleon-6',
'chameleon-11',
'chameleon-20',
'crocodile-3',
'crocodile-4',
'crocodile-10',
'crocodile-14',
'gecko-1',
'gecko-5',
'gecko-16',
'gecko-19',
'fox-2',
'fox-3',
'fox-5',
'fox-20',
'giraffe-2',
'giraffe-10',
'giraffe-13',
'giraffe-15',
'gorilla-4',
'gorilla-6',
'gorilla-9',
'gorilla-13',
'hippo-1',
'hippo-7',
'hippo-9',
'hippo-20',
'horse-1',
'horse-4',
'horse-12',
'horse-15',
'kangaroo-2',
'kangaroo-5',
'kangaroo-11',
'kangaroo-14',
'leopard-1',
'leopard-7',
'leopard-16',
'leopard-20',
'lion-1',
'lion-5',
'lion-12',
'lion-20',
'lizard-1',
'lizard-3',
'lizard-6',
'lizard-13',
'microphone-2',
'microphone-6',
'microphone-14',
'microphone-16',
'monkey-3',
'monkey-4',
'monkey-9',
'monkey-17',
'shark-2',
'shark-3',
'shark-5',
'shark-6',
'squirrel-8',
'squirrel-11',
'squirrel-13',
'squirrel-19',
'volleyball-1',
'volleyball-13',
'volleyball-18',
'volleyball-19']
return sequence_list
lib/test/evaluation/lasotextensionsubsetdataset.py 0 → 100644
View file @ 75d93027
import numpy as np
from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList
from lib.test.utils.load_text import load_text
class LaSOTExtensionSubsetDataset(BaseDataset):
"""
LaSOT test set consisting of 280 videos (see Protocol-II in the LaSOT paper)
Publication:
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
Heng Fan, Hexin Bai, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Harshit, Mingzhen Huang, Juehuan Liu,
Yong Xu, Chunyuan Liao, Lin Yuan, Haibin Ling
IJCV, 2020
https://arxiv.org/pdf/2009.03465.pdf
Download the dataset from http://vision.cs.stonybrook.edu/~lasot/download.html
"""
def __init__(self):
super().__init__()
self.base_path = self.env_settings.lasot_extension_subset_path
self.sequence_list = self._get_sequence_list()
self.clean_list = self.clean_seq_list()
def clean_seq_list(self):
clean_lst = []
for i in range(len(self.sequence_list)):
cls, _ = self.sequence_list[i].split('-')
clean_lst.append(cls)
return clean_lst
def get_sequence_list(self):
return SequenceList([self._construct_sequence(s) for s in self.sequence_list])
def _construct_sequence(self, sequence_name):
class_name = sequence_name.split('-')[0]
anno_path = '{}/{}/{}/groundtruth.txt'.format(self.base_path, class_name, sequence_name)
ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64)
occlusion_label_path = '{}/{}/{}/full_occlusion.txt'.format(self.base_path, class_name, sequence_name)
# NOTE: pandas backed seems super super slow for loading occlusion/oov masks
full_occlusion = load_text(str(occlusion_label_path), delimiter=',', dtype=np.float64, backend='numpy')
out_of_view_label_path = '{}/{}/{}/out_of_view.txt'.format(self.base_path, class_name, sequence_name)
out_of_view = load_text(str(out_of_view_label_path), delimiter=',', dtype=np.float64, backend='numpy')
target_visible = np.logical_and(full_occlusion == 0, out_of_view == 0)
frames_path = '{}/{}/{}/img'.format(self.base_path, class_name, sequence_name)
frames_list = ['{}/{:08d}.jpg'.format(frames_path, frame_number) for frame_number in range(1, ground_truth_rect.shape[0] + 1)]
target_class = class_name
return Sequence(sequence_name, frames_list, 'lasot_extension_subset', ground_truth_rect.reshape(-1, 4),
object_class=target_class, target_visible=target_visible)
def __len__(self):
return len(self.sequence_list)
def _get_sequence_list(self):
sequence_list = ['atv-1',
'atv-2',
'atv-3',
'atv-4',
'atv-5',
'atv-6',
'atv-7',
'atv-8',
'atv-9',
'atv-10',
'badminton-1',
'badminton-2',
'badminton-3',
'badminton-4',
'badminton-5',
'badminton-6',
'badminton-7',
'badminton-8',
'badminton-9',
'badminton-10',
'cosplay-1',
'cosplay-10',
'cosplay-2',
'cosplay-3',
'cosplay-4',
'cosplay-5',
'cosplay-6',
'cosplay-7',
'cosplay-8',
'cosplay-9',
'dancingshoe-1',
'dancingshoe-2',
'dancingshoe-3',
'dancingshoe-4',
'dancingshoe-5',
'dancingshoe-6',
'dancingshoe-7',
'dancingshoe-8',
'dancingshoe-9',
'dancingshoe-10',
'footbag-1',
'footbag-2',
'footbag-3',
'footbag-4',
'footbag-5',
'footbag-6',
'footbag-7',
'footbag-8',
'footbag-9',
'footbag-10',
'frisbee-1',
'frisbee-2',
'frisbee-3',
'frisbee-4',
'frisbee-5',
'frisbee-6',
'frisbee-7',
'frisbee-8',
'frisbee-9',
'frisbee-10',
'jianzi-1',
'jianzi-2',
'jianzi-3',
'jianzi-4',
'jianzi-5',
'jianzi-6',
'jianzi-7',
'jianzi-8',
'jianzi-9',
'jianzi-10',
'lantern-1',
'lantern-2',
'lantern-3',
'lantern-4',
'lantern-5',
'lantern-6',
'lantern-7',
'lantern-8',
'lantern-9',
'lantern-10',
'misc-1',
'misc-2',
'misc-3',
'misc-4',
'misc-5',
'misc-6',
'misc-7',
'misc-8',
'misc-9',
'misc-10',
'opossum-1',
'opossum-2',
'opossum-3',
'opossum-4',
'opossum-5',
'opossum-6',
'opossum-7',
'opossum-8',
'opossum-9',
'opossum-10',
'paddle-1',
'paddle-2',
'paddle-3',
'paddle-4',
'paddle-5',
'paddle-6',
'paddle-7',
'paddle-8',
'paddle-9',
'paddle-10',
'raccoon-1',
'raccoon-2',
'raccoon-3',
'raccoon-4',
'raccoon-5',
'raccoon-6',
'raccoon-7',
'raccoon-8',
'raccoon-9',
'raccoon-10',
'rhino-1',
'rhino-2',
'rhino-3',
'rhino-4',
'rhino-5',
'rhino-6',
'rhino-7',
'rhino-8',
'rhino-9',
'rhino-10',
'skatingshoe-1',
'skatingshoe-2',
'skatingshoe-3',
'skatingshoe-4',
'skatingshoe-5',
'skatingshoe-6',
'skatingshoe-7',
'skatingshoe-8',
'skatingshoe-9',
'skatingshoe-10',
'wingsuit-1',
'wingsuit-2',
'wingsuit-3',
'wingsuit-4',
'wingsuit-5',
'wingsuit-6',
'wingsuit-7',
'wingsuit-8',
'wingsuit-9',
'wingsuit-10']
return sequence_list
\ No newline at end of file
lib/test/evaluation/local.py 0 → 100644
View file @ 75d93027
from lib.test.evaluation.environment import EnvSettings
def local_env_settings():
settings = EnvSettings()
# Set your local paths here.
settings.lasot_path = '/home/cycyang/code/vot-sam/data/LaSOT'
settings.lasot_extension_subset_path = '/home/cycyang/code/vot-sam/data/LaSOT-ext'
settings.nfs_path = '/home/cycyang/code/vot-sam/data/NFS'
settings.otb_path = '/home/cycyang/code/vot-sam/data/otb'
settings.uav_path = '//home/cycyang/code/vot-sam/data/uav'
settings.results_path = '/home/cycyang/code/vot-sam/raw_results'
settings.result_plot_path = '/home/cycyang/code/vot-sam/evaluation_results'
settings.save_dir = '/home/cycyang/code/vot-sam/evaluation_results'
settings.davis_dir = ''
settings.got10k_lmdb_path = '/home/baiyifan/code/OSTrack/data/got10k_lmdb'
settings.got10k_path = '/home/baiyifan/GOT-10k'
settings.got_packed_results_path = ''
settings.got_reports_path = ''
settings.itb_path = '/home/baiyifan/code/OSTrack/data/itb'
settings.lasot_lmdb_path = '/home/baiyifan/code/OSTrack/data/lasot_lmdb'
settings.network_path = '/ssddata/baiyifan/artrack_256_full_re/' # Where tracking networks are stored.
settings.prj_dir = '/home/baiyifan/code/2d_autoregressive/bins_mask'
settings.segmentation_path = '/data1/os/test/segmentation_results'
settings.tc128_path = '/home/baiyifan/code/OSTrack/data/TC128'
settings.tn_packed_results_path = ''
settings.tnl2k_path = '/home/baiyifan/code/OSTrack/data/tnl2k'
settings.tpl_path = ''
settings.trackingnet_path = '/ssddata/TrackingNet/all_zip'
settings.vot18_path = '/home/baiyifan/code/OSTrack/data/vot2018'
settings.vot22_path = '/home/baiyifan/code/OSTrack/data/vot2022'
settings.vot_path = '/home/baiyifan/code/OSTrack/data/VOT2019'
settings.youtubevos_dir = ''
return settings
lib/test/evaluation/nfsdataset.py 0 → 100644
View file @ 75d93027
import numpy as np
from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList
from lib.test.utils.load_text import load_text
class NFSDataset(BaseDataset):
""" NFS dataset.
Publication:
Need for Speed: A Benchmark for Higher Frame Rate Object Tracking
H. Kiani Galoogahi, A. Fagg, C. Huang, D. Ramanan, and S.Lucey
ICCV, 2017
http://openaccess.thecvf.com/content_ICCV_2017/papers/Galoogahi_Need_for_Speed_ICCV_2017_paper.pdf
Download the dataset from http://ci2cv.net/nfs/index.html
"""
def __init__(self):
super().__init__()
self.base_path = self.env_settings.nfs_path
self.sequence_info_list = self._get_sequence_info_list()
def get_sequence_list(self):
return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list])
def _construct_sequence(self, sequence_info):
sequence_path = sequence_info['path']
nz = sequence_info['nz']
ext = sequence_info['ext']
start_frame = sequence_info['startFrame']
end_frame = sequence_info['endFrame']
init_omit = 0
if 'initOmit' in sequence_info:
init_omit = sequence_info['initOmit']
frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path,
sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame+init_omit, end_frame+1)]
# anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path'])
anno_path = f"{self.base_path}/{sequence_info['name'][4:]}/30/groundtruth.txt"
# ground_truth_rect = load_text(str(anno_path), delimiter='\t', dtype=np.float64)
ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64)
return Sequence(sequence_info['name'][4:], frames, 'nfs', ground_truth_rect[init_omit:,:],
object_class=sequence_info['object_class'])
def __len__(self):
return len(self.sequence_info_list)
def _get_sequence_info_list(self):
sequence_info_list = [
{"name": "nfs_Gymnastics", "path": "sequences/Gymnastics", "startFrame": 1, "endFrame": 368, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_Gymnastics.txt", "object_class": "person", 'occlusion': False},
{"name": "nfs_MachLoop_jet", "path": "sequences/MachLoop_jet", "startFrame": 1, "endFrame": 99, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_MachLoop_jet.txt", "object_class": "aircraft", 'occlusion': False},
{"name": "nfs_Skiing_red", "path": "sequences/Skiing_red", "startFrame": 1, "endFrame": 69, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_Skiing_red.txt", "object_class": "person", 'occlusion': False},
{"name": "nfs_Skydiving", "path": "sequences/Skydiving", "startFrame": 1, "endFrame": 196, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_Skydiving.txt", "object_class": "person", 'occlusion': True},
{"name": "nfs_airboard_1", "path": "sequences/airboard_1", "startFrame": 1, "endFrame": 425, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_airboard_1.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_airplane_landing", "path": "sequences/airplane_landing", "startFrame": 1, "endFrame": 81, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_airplane_landing.txt", "object_class": "aircraft", 'occlusion': False},
{"name": "nfs_airtable_3", "path": "sequences/airtable_3", "startFrame": 1, "endFrame": 482, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_airtable_3.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_basketball_1", "path": "sequences/basketball_1", "startFrame": 1, "endFrame": 282, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_1.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_basketball_2", "path": "sequences/basketball_2", "startFrame": 1, "endFrame": 102, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_2.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_basketball_3", "path": "sequences/basketball_3", "startFrame": 1, "endFrame": 421, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_3.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_basketball_6", "path": "sequences/basketball_6", "startFrame": 1, "endFrame": 224, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_6.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_basketball_7", "path": "sequences/basketball_7", "startFrame": 1, "endFrame": 240, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_7.txt", "object_class": "person", 'occlusion': True},
{"name": "nfs_basketball_player", "path": "sequences/basketball_player", "startFrame": 1, "endFrame": 369, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_player.txt", "object_class": "person", 'occlusion': True},
{"name": "nfs_basketball_player_2", "path": "sequences/basketball_player_2", "startFrame": 1, "endFrame": 437, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_basketball_player_2.txt", "object_class": "person", 'occlusion': False},
{"name": "nfs_beach_flipback_person", "path": "sequences/beach_flipback_person", "startFrame": 1, "endFrame": 61, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_beach_flipback_person.txt", "object_class": "person head", 'occlusion': False},
{"name": "nfs_bee", "path": "sequences/bee", "startFrame": 1, "endFrame": 45, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bee.txt", "object_class": "insect", 'occlusion': False},
{"name": "nfs_biker_acrobat", "path": "sequences/biker_acrobat", "startFrame": 1, "endFrame": 128, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_acrobat.txt", "object_class": "bicycle", 'occlusion': False},
{"name": "nfs_biker_all_1", "path": "sequences/biker_all_1", "startFrame": 1, "endFrame": 113, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_all_1.txt", "object_class": "person", 'occlusion': False},
{"name": "nfs_biker_head_2", "path": "sequences/biker_head_2", "startFrame": 1, "endFrame": 132, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_head_2.txt", "object_class": "person head", 'occlusion': False},
{"name": "nfs_biker_head_3", "path": "sequences/biker_head_3", "startFrame": 1, "endFrame": 254, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_head_3.txt", "object_class": "person head", 'occlusion': False},
{"name": "nfs_biker_upper_body", "path": "sequences/biker_upper_body", "startFrame": 1, "endFrame": 194, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_upper_body.txt", "object_class": "person", 'occlusion': False},
{"name": "nfs_biker_whole_body", "path": "sequences/biker_whole_body", "startFrame": 1, "endFrame": 572, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_biker_whole_body.txt", "object_class": "person", 'occlusion': True},
{"name": "nfs_billiard_2", "path": "sequences/billiard_2", "startFrame": 1, "endFrame": 604, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_2.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_billiard_3", "path": "sequences/billiard_3", "startFrame": 1, "endFrame": 698, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_3.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_billiard_6", "path": "sequences/billiard_6", "startFrame": 1, "endFrame": 771, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_6.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_billiard_7", "path": "sequences/billiard_7", "startFrame": 1, "endFrame": 724, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_7.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_billiard_8", "path": "sequences/billiard_8", "startFrame": 1, "endFrame": 778, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_billiard_8.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_bird_2", "path": "sequences/bird_2", "startFrame": 1, "endFrame": 476, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bird_2.txt", "object_class": "bird", 'occlusion': False},
{"name": "nfs_book", "path": "sequences/book", "startFrame": 1, "endFrame": 288, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_book.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_bottle", "path": "sequences/bottle", "startFrame": 1, "endFrame": 2103, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bottle.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_bowling_1", "path": "sequences/bowling_1", "startFrame": 1, "endFrame": 303, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_1.txt", "object_class": "ball", 'occlusion': True},
{"name": "nfs_bowling_2", "path": "sequences/bowling_2", "startFrame": 1, "endFrame": 710, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_2.txt", "object_class": "ball", 'occlusion': True},
{"name": "nfs_bowling_3", "path": "sequences/bowling_3", "startFrame": 1, "endFrame": 271, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_3.txt", "object_class": "ball", 'occlusion': True},
{"name": "nfs_bowling_6", "path": "sequences/bowling_6", "startFrame": 1, "endFrame": 260, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_6.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_bowling_ball", "path": "sequences/bowling_ball", "startFrame": 1, "endFrame": 275, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bowling_ball.txt", "object_class": "ball", 'occlusion': True},
{"name": "nfs_bunny", "path": "sequences/bunny", "startFrame": 1, "endFrame": 705, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_bunny.txt", "object_class": "mammal", 'occlusion': False},
{"name": "nfs_car", "path": "sequences/car", "startFrame": 1, "endFrame": 2020, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car.txt", "object_class": "car", 'occlusion': True},
{"name": "nfs_car_camaro", "path": "sequences/car_camaro", "startFrame": 1, "endFrame": 36, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_camaro.txt", "object_class": "car", 'occlusion': False},
{"name": "nfs_car_drifting", "path": "sequences/car_drifting", "startFrame": 1, "endFrame": 173, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_drifting.txt", "object_class": "car", 'occlusion': False},
{"name": "nfs_car_jumping", "path": "sequences/car_jumping", "startFrame": 1, "endFrame": 22, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_jumping.txt", "object_class": "car", 'occlusion': False},
{"name": "nfs_car_rc_rolling", "path": "sequences/car_rc_rolling", "startFrame": 1, "endFrame": 62, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_rc_rolling.txt", "object_class": "car", 'occlusion': False},
{"name": "nfs_car_rc_rotating", "path": "sequences/car_rc_rotating", "startFrame": 1, "endFrame": 80, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_rc_rotating.txt", "object_class": "car", 'occlusion': False},
{"name": "nfs_car_side", "path": "sequences/car_side", "startFrame": 1, "endFrame": 108, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_side.txt", "object_class": "car", 'occlusion': False},
{"name": "nfs_car_white", "path": "sequences/car_white", "startFrame": 1, "endFrame": 2063, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_car_white.txt", "object_class": "car", 'occlusion': False},
{"name": "nfs_cheetah", "path": "sequences/cheetah", "startFrame": 1, "endFrame": 167, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_cheetah.txt", "object_class": "mammal", 'occlusion': True},
{"name": "nfs_cup", "path": "sequences/cup", "startFrame": 1, "endFrame": 1281, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_cup.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_cup_2", "path": "sequences/cup_2", "startFrame": 1, "endFrame": 182, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_cup_2.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_dog", "path": "sequences/dog", "startFrame": 1, "endFrame": 1030, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dog.txt", "object_class": "dog", 'occlusion': True},
{"name": "nfs_dog_1", "path": "sequences/dog_1", "startFrame": 1, "endFrame": 168, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dog_1.txt", "object_class": "dog", 'occlusion': False},
# {"name": "nfs_dog_2", "path": "sequences/dog_2", "startFrame": 1, "endFrame": 594, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dog_2.txt", "object_class": "dog", 'occlusion': True},
{"name": "nfs_dog_3", "path": "sequences/dog_3", "startFrame": 1, "endFrame": 200, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dog_3.txt", "object_class": "dog", 'occlusion': False},
{"name": "nfs_dogs", "path": "sequences/dogs", "startFrame": 1, "endFrame": 198, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dogs.txt", "object_class": "dog", 'occlusion': True},
{"name": "nfs_dollar", "path": "sequences/dollar", "startFrame": 1, "endFrame": 1426, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_dollar.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_drone", "path": "sequences/drone", "startFrame": 1, "endFrame": 70, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_drone.txt", "object_class": "aircraft", 'occlusion': False},
{"name": "nfs_ducks_lake", "path": "sequences/ducks_lake", "startFrame": 1, "endFrame": 107, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_ducks_lake.txt", "object_class": "bird", 'occlusion': False},
{"name": "nfs_exit", "path": "sequences/exit", "startFrame": 1, "endFrame": 359, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_exit.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_first", "path": "sequences/first", "startFrame": 1, "endFrame": 435, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_first.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_flower", "path": "sequences/flower", "startFrame": 1, "endFrame": 448, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_flower.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_footbal_skill", "path": "sequences/footbal_skill", "startFrame": 1, "endFrame": 131, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_footbal_skill.txt", "object_class": "ball", 'occlusion': True},
{"name": "nfs_helicopter", "path": "sequences/helicopter", "startFrame": 1, "endFrame": 310, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_helicopter.txt", "object_class": "aircraft", 'occlusion': False},
{"name": "nfs_horse_jumping", "path": "sequences/horse_jumping", "startFrame": 1, "endFrame": 117, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_horse_jumping.txt", "object_class": "horse", 'occlusion': True},
{"name": "nfs_horse_running", "path": "sequences/horse_running", "startFrame": 1, "endFrame": 139, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_horse_running.txt", "object_class": "horse", 'occlusion': False},
{"name": "nfs_iceskating_6", "path": "sequences/iceskating_6", "startFrame": 1, "endFrame": 603, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_iceskating_6.txt", "object_class": "person", 'occlusion': False},
{"name": "nfs_jellyfish_5", "path": "sequences/jellyfish_5", "startFrame": 1, "endFrame": 746, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_jellyfish_5.txt", "object_class": "invertebrate", 'occlusion': False},
{"name": "nfs_kid_swing", "path": "sequences/kid_swing", "startFrame": 1, "endFrame": 169, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_kid_swing.txt", "object_class": "person", 'occlusion': False},
{"name": "nfs_motorcross", "path": "sequences/motorcross", "startFrame": 1, "endFrame": 39, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_motorcross.txt", "object_class": "vehicle", 'occlusion': True},
{"name": "nfs_motorcross_kawasaki", "path": "sequences/motorcross_kawasaki", "startFrame": 1, "endFrame": 65, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_motorcross_kawasaki.txt", "object_class": "vehicle", 'occlusion': False},
{"name": "nfs_parkour", "path": "sequences/parkour", "startFrame": 1, "endFrame": 58, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_parkour.txt", "object_class": "person head", 'occlusion': False},
{"name": "nfs_person_scooter", "path": "sequences/person_scooter", "startFrame": 1, "endFrame": 413, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_person_scooter.txt", "object_class": "person", 'occlusion': True},
{"name": "nfs_pingpong_2", "path": "sequences/pingpong_2", "startFrame": 1, "endFrame": 1277, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_pingpong_2.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_pingpong_7", "path": "sequences/pingpong_7", "startFrame": 1, "endFrame": 1290, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_pingpong_7.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_pingpong_8", "path": "sequences/pingpong_8", "startFrame": 1, "endFrame": 296, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_pingpong_8.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_purse", "path": "sequences/purse", "startFrame": 1, "endFrame": 968, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_purse.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_rubber", "path": "sequences/rubber", "startFrame": 1, "endFrame": 1328, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_rubber.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_running", "path": "sequences/running", "startFrame": 1, "endFrame": 677, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_running.txt", "object_class": "person", 'occlusion': False},
{"name": "nfs_running_100_m", "path": "sequences/running_100_m", "startFrame": 1, "endFrame": 313, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_running_100_m.txt", "object_class": "person", 'occlusion': True},
{"name": "nfs_running_100_m_2", "path": "sequences/running_100_m_2", "startFrame": 1, "endFrame": 337, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_running_100_m_2.txt", "object_class": "person", 'occlusion': True},
{"name": "nfs_running_2", "path": "sequences/running_2", "startFrame": 1, "endFrame": 363, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_running_2.txt", "object_class": "person", 'occlusion': False},
{"name": "nfs_shuffleboard_1", "path": "sequences/shuffleboard_1", "startFrame": 1, "endFrame": 42, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_1.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_shuffleboard_2", "path": "sequences/shuffleboard_2", "startFrame": 1, "endFrame": 41, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_2.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_shuffleboard_4", "path": "sequences/shuffleboard_4", "startFrame": 1, "endFrame": 62, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_4.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_shuffleboard_5", "path": "sequences/shuffleboard_5", "startFrame": 1, "endFrame": 32, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_5.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_shuffleboard_6", "path": "sequences/shuffleboard_6", "startFrame": 1, "endFrame": 52, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffleboard_6.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_shuffletable_2", "path": "sequences/shuffletable_2", "startFrame": 1, "endFrame": 372, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffletable_2.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_shuffletable_3", "path": "sequences/shuffletable_3", "startFrame": 1, "endFrame": 368, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffletable_3.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_shuffletable_4", "path": "sequences/shuffletable_4", "startFrame": 1, "endFrame": 101, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_shuffletable_4.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_ski_long", "path": "sequences/ski_long", "startFrame": 1, "endFrame": 274, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_ski_long.txt", "object_class": "person", 'occlusion': False},
{"name": "nfs_soccer_ball", "path": "sequences/soccer_ball", "startFrame": 1, "endFrame": 163, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_ball.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_soccer_ball_2", "path": "sequences/soccer_ball_2", "startFrame": 1, "endFrame": 1934, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_ball_2.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_soccer_ball_3", "path": "sequences/soccer_ball_3", "startFrame": 1, "endFrame": 1381, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_ball_3.txt", "object_class": "ball", 'occlusion': False},
{"name": "nfs_soccer_player_2", "path": "sequences/soccer_player_2", "startFrame": 1, "endFrame": 475, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_player_2.txt", "object_class": "person", 'occlusion': False},
{"name": "nfs_soccer_player_3", "path": "sequences/soccer_player_3", "startFrame": 1, "endFrame": 319, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_soccer_player_3.txt", "object_class": "person", 'occlusion': True},
{"name": "nfs_stop_sign", "path": "sequences/stop_sign", "startFrame": 1, "endFrame": 302, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_stop_sign.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_suv", "path": "sequences/suv", "startFrame": 1, "endFrame": 2584, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_suv.txt", "object_class": "car", 'occlusion': False},
{"name": "nfs_tiger", "path": "sequences/tiger", "startFrame": 1, "endFrame": 1556, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_tiger.txt", "object_class": "mammal", 'occlusion': False},
{"name": "nfs_walking", "path": "sequences/walking", "startFrame": 1, "endFrame": 555, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_walking.txt", "object_class": "person", 'occlusion': False},
{"name": "nfs_walking_3", "path": "sequences/walking_3", "startFrame": 1, "endFrame": 1427, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_walking_3.txt", "object_class": "person", 'occlusion': False},
{"name": "nfs_water_ski_2", "path": "sequences/water_ski_2", "startFrame": 1, "endFrame": 47, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_water_ski_2.txt", "object_class": "person", 'occlusion': False},
{"name": "nfs_yoyo", "path": "sequences/yoyo", "startFrame": 1, "endFrame": 67, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_yoyo.txt", "object_class": "other", 'occlusion': False},
{"name": "nfs_zebra_fish", "path": "sequences/zebra_fish", "startFrame": 1, "endFrame": 671, "nz": 5, "ext": "jpg", "anno_path": "anno/nfs_zebra_fish.txt", "object_class": "fish", 'occlusion': False},
]
return sequence_info_list
\ No newline at end of file
lib/test/evaluation/otbdataset.py 0 → 100644
View file @ 75d93027
import numpy as np
from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList
from lib.test.utils.load_text import load_text
class OTBDataset(BaseDataset):
""" OTB-2015 dataset
Publication:
Object Tracking Benchmark
Wu, Yi, Jongwoo Lim, and Ming-hsuan Yan
TPAMI, 2015
http://faculty.ucmerced.edu/mhyang/papers/pami15_tracking_benchmark.pdf
Download the dataset from http://cvlab.hanyang.ac.kr/tracker_benchmark/index.html
"""
def __init__(self):
super().__init__()
self.base_path = self.env_settings.otb_path
self.sequence_info_list = self._get_sequence_info_list()
def get_sequence_list(self):
return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list])
def _construct_sequence(self, sequence_info):
sequence_path = sequence_info['path']
nz = sequence_info['nz']
ext = sequence_info['ext']
start_frame = sequence_info['startFrame']
end_frame = sequence_info['endFrame']
init_omit = 0
if 'initOmit' in sequence_info:
init_omit = sequence_info['initOmit']
frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path,
sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame+init_omit, end_frame+1)]
# anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path'])
anno_path = '{}/{}/groundtruth.txt'.format(self.base_path, sequence_info['name'])
# NOTE: OTB has some weird annos which panda cannot handle
ground_truth_rect = load_text(str(anno_path), delimiter=(',', None), dtype=np.float64, backend='numpy')
return Sequence(sequence_info['name'], frames, 'otb', ground_truth_rect[init_omit:,:],
object_class=sequence_info['object_class'])
def __len__(self):
return len(self.sequence_info_list)
def _get_sequence_info_list(self):
sequence_info_list = [
{"name": "Basketball", "path": "Basketball/img", "startFrame": 1, "endFrame": 725, "nz": 4, "ext": "jpg", "anno_path": "Basketball/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Biker", "path": "Biker/img", "startFrame": 1, "endFrame": 142, "nz": 4, "ext": "jpg", "anno_path": "Biker/groundtruth_rect.txt",
"object_class": "person head"},
{"name": "Bird1", "path": "Bird1/img", "startFrame": 1, "endFrame": 408, "nz": 4, "ext": "jpg", "anno_path": "Bird1/groundtruth_rect.txt",
"object_class": "bird"},
{"name": "Bird2", "path": "Bird2/img", "startFrame": 1, "endFrame": 99, "nz": 4, "ext": "jpg", "anno_path": "Bird2/groundtruth_rect.txt",
"object_class": "bird"},
{"name": "BlurBody", "path": "BlurBody/img", "startFrame": 1, "endFrame": 334, "nz": 4, "ext": "jpg", "anno_path": "BlurBody/groundtruth_rect.txt",
"object_class": "person"},
{"name": "BlurCar1", "path": "BlurCar1/img", "startFrame": 247, "endFrame": 988, "nz": 4, "ext": "jpg", "anno_path": "BlurCar1/groundtruth_rect.txt",
"object_class": "car"},
{"name": "BlurCar2", "path": "BlurCar2/img", "startFrame": 1, "endFrame": 585, "nz": 4, "ext": "jpg", "anno_path": "BlurCar2/groundtruth_rect.txt",
"object_class": "car"},
{"name": "BlurCar3", "path": "BlurCar3/img", "startFrame": 3, "endFrame": 359, "nz": 4, "ext": "jpg", "anno_path": "BlurCar3/groundtruth_rect.txt",
"object_class": "car"},
{"name": "BlurCar4", "path": "BlurCar4/img", "startFrame": 18, "endFrame": 397, "nz": 4, "ext": "jpg", "anno_path": "BlurCar4/groundtruth_rect.txt",
"object_class": "car"},
{"name": "BlurFace", "path": "BlurFace/img", "startFrame": 1, "endFrame": 493, "nz": 4, "ext": "jpg", "anno_path": "BlurFace/groundtruth_rect.txt",
"object_class": "face"},
{"name": "BlurOwl", "path": "BlurOwl/img", "startFrame": 1, "endFrame": 631, "nz": 4, "ext": "jpg", "anno_path": "BlurOwl/groundtruth_rect.txt",
"object_class": "other"},
{"name": "Board", "path": "Board/img", "startFrame": 1, "endFrame": 698, "nz": 5, "ext": "jpg", "anno_path": "Board/groundtruth_rect.txt",
"object_class": "other"},
{"name": "Bolt", "path": "Bolt/img", "startFrame": 1, "endFrame": 350, "nz": 4, "ext": "jpg", "anno_path": "Bolt/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Bolt2", "path": "Bolt2/img", "startFrame": 1, "endFrame": 293, "nz": 4, "ext": "jpg", "anno_path": "Bolt2/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Box", "path": "Box/img", "startFrame": 1, "endFrame": 1161, "nz": 4, "ext": "jpg", "anno_path": "Box/groundtruth_rect.txt",
"object_class": "other"},
{"name": "Boy", "path": "Boy/img", "startFrame": 1, "endFrame": 602, "nz": 4, "ext": "jpg", "anno_path": "Boy/groundtruth_rect.txt",
"object_class": "face"},
{"name": "Car1", "path": "Car1/img", "startFrame": 1, "endFrame": 1020, "nz": 4, "ext": "jpg", "anno_path": "Car1/groundtruth_rect.txt",
"object_class": "car"},
{"name": "Car2", "path": "Car2/img", "startFrame": 1, "endFrame": 913, "nz": 4, "ext": "jpg", "anno_path": "Car2/groundtruth_rect.txt",
"object_class": "car"},
{"name": "Car24", "path": "Car24/img", "startFrame": 1, "endFrame": 3059, "nz": 4, "ext": "jpg", "anno_path": "Car24/groundtruth_rect.txt",
"object_class": "car"},
{"name": "Car4", "path": "Car4/img", "startFrame": 1, "endFrame": 659, "nz": 4, "ext": "jpg", "anno_path": "Car4/groundtruth_rect.txt",
"object_class": "car"},
{"name": "CarDark", "path": "CarDark/img", "startFrame": 1, "endFrame": 393, "nz": 4, "ext": "jpg", "anno_path": "CarDark/groundtruth_rect.txt",
"object_class": "car"},
{"name": "CarScale", "path": "CarScale/img", "startFrame": 1, "endFrame": 252, "nz": 4, "ext": "jpg", "anno_path": "CarScale/groundtruth_rect.txt",
"object_class": "car"},
{"name": "ClifBar", "path": "ClifBar/img", "startFrame": 1, "endFrame": 472, "nz": 4, "ext": "jpg", "anno_path": "ClifBar/groundtruth_rect.txt",
"object_class": "other"},
{"name": "Coke", "path": "Coke/img", "startFrame": 1, "endFrame": 291, "nz": 4, "ext": "jpg", "anno_path": "Coke/groundtruth_rect.txt",
"object_class": "other"},
{"name": "Couple", "path": "Couple/img", "startFrame": 1, "endFrame": 140, "nz": 4, "ext": "jpg", "anno_path": "Couple/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Coupon", "path": "Coupon/img", "startFrame": 1, "endFrame": 327, "nz": 4, "ext": "jpg", "anno_path": "Coupon/groundtruth_rect.txt",
"object_class": "other"},
{"name": "Crossing", "path": "Crossing/img", "startFrame": 1, "endFrame": 120, "nz": 4, "ext": "jpg", "anno_path": "Crossing/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Crowds", "path": "Crowds/img", "startFrame": 1, "endFrame": 347, "nz": 4, "ext": "jpg", "anno_path": "Crowds/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Dancer", "path": "Dancer/img", "startFrame": 1, "endFrame": 225, "nz": 4, "ext": "jpg", "anno_path": "Dancer/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Dancer2", "path": "Dancer2/img", "startFrame": 1, "endFrame": 150, "nz": 4, "ext": "jpg", "anno_path": "Dancer2/groundtruth_rect.txt",
"object_class": "person"},
{"name": "David", "path": "David/img", "startFrame": 300, "endFrame": 770, "nz": 4, "ext": "jpg", "anno_path": "David/groundtruth_rect.txt",
"object_class": "face"},
{"name": "David2", "path": "David2/img", "startFrame": 1, "endFrame": 537, "nz": 4, "ext": "jpg", "anno_path": "David2/groundtruth_rect.txt",
"object_class": "face"},
{"name": "David3", "path": "David3/img", "startFrame": 1, "endFrame": 252, "nz": 4, "ext": "jpg", "anno_path": "David3/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Deer", "path": "Deer/img", "startFrame": 1, "endFrame": 71, "nz": 4, "ext": "jpg", "anno_path": "Deer/groundtruth_rect.txt",
"object_class": "mammal"},
{"name": "Diving", "path": "Diving/img", "startFrame": 1, "endFrame": 215, "nz": 4, "ext": "jpg", "anno_path": "Diving/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Dog", "path": "Dog/img", "startFrame": 1, "endFrame": 127, "nz": 4, "ext": "jpg", "anno_path": "Dog/groundtruth_rect.txt",
"object_class": "dog"},
{"name": "Dog1", "path": "Dog1/img", "startFrame": 1, "endFrame": 1350, "nz": 4, "ext": "jpg", "anno_path": "Dog1/groundtruth_rect.txt",
"object_class": "dog"},
{"name": "Doll", "path": "Doll/img", "startFrame": 1, "endFrame": 3872, "nz": 4, "ext": "jpg", "anno_path": "Doll/groundtruth_rect.txt",
"object_class": "other"},
{"name": "DragonBaby", "path": "DragonBaby/img", "startFrame": 1, "endFrame": 113, "nz": 4, "ext": "jpg", "anno_path": "DragonBaby/groundtruth_rect.txt",
"object_class": "face"},
{"name": "Dudek", "path": "Dudek/img", "startFrame": 1, "endFrame": 1145, "nz": 4, "ext": "jpg", "anno_path": "Dudek/groundtruth_rect.txt",
"object_class": "face"},
{"name": "FaceOcc1", "path": "FaceOcc1/img", "startFrame": 1, "endFrame": 892, "nz": 4, "ext": "jpg", "anno_path": "FaceOcc1/groundtruth_rect.txt",
"object_class": "face"},
{"name": "FaceOcc2", "path": "FaceOcc2/img", "startFrame": 1, "endFrame": 812, "nz": 4, "ext": "jpg", "anno_path": "FaceOcc2/groundtruth_rect.txt",
"object_class": "face"},
{"name": "Fish", "path": "Fish/img", "startFrame": 1, "endFrame": 476, "nz": 4, "ext": "jpg", "anno_path": "Fish/groundtruth_rect.txt",
"object_class": "other"},
{"name": "FleetFace", "path": "FleetFace/img", "startFrame": 1, "endFrame": 707, "nz": 4, "ext": "jpg", "anno_path": "FleetFace/groundtruth_rect.txt",
"object_class": "face"},
{"name": "Football", "path": "Football/img", "startFrame": 1, "endFrame": 362, "nz": 4, "ext": "jpg", "anno_path": "Football/groundtruth_rect.txt",
"object_class": "person head"},
{"name": "Football1", "path": "Football1/img", "startFrame": 1, "endFrame": 74, "nz": 4, "ext": "jpg", "anno_path": "Football1/groundtruth_rect.txt",
"object_class": "face"},
{"name": "Freeman1", "path": "Freeman1/img", "startFrame": 1, "endFrame": 326, "nz": 4, "ext": "jpg", "anno_path": "Freeman1/groundtruth_rect.txt",
"object_class": "face"},
{"name": "Freeman3", "path": "Freeman3/img", "startFrame": 1, "endFrame": 460, "nz": 4, "ext": "jpg", "anno_path": "Freeman3/groundtruth_rect.txt",
"object_class": "face"},
{"name": "Freeman4", "path": "Freeman4/img", "startFrame": 1, "endFrame": 283, "nz": 4, "ext": "jpg", "anno_path": "Freeman4/groundtruth_rect.txt",
"object_class": "face"},
{"name": "Girl", "path": "Girl/img", "startFrame": 1, "endFrame": 500, "nz": 4, "ext": "jpg", "anno_path": "Girl/groundtruth_rect.txt",
"object_class": "face"},
{"name": "Girl2", "path": "Girl2/img", "startFrame": 1, "endFrame": 1500, "nz": 4, "ext": "jpg", "anno_path": "Girl2/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Gym", "path": "Gym/img", "startFrame": 1, "endFrame": 767, "nz": 4, "ext": "jpg", "anno_path": "Gym/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Human2", "path": "Human2/img", "startFrame": 1, "endFrame": 1128, "nz": 4, "ext": "jpg", "anno_path": "Human2/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Human3", "path": "Human3/img", "startFrame": 1, "endFrame": 1698, "nz": 4, "ext": "jpg", "anno_path": "Human3/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Human4_2", "path": "Human4/img", "startFrame": 1, "endFrame": 667, "nz": 4, "ext": "jpg", "anno_path": "Human4/groundtruth_rect.2.txt",
"object_class": "person"},
{"name": "Human4", "path": "Human4/img", "startFrame": 1, "endFrame": 667, "nz": 4, "ext": "jpg", "anno_path": "Human4/groundtruth_rect.2.txt",
"object_class": "person"},
{"name": "Human5", "path": "Human5/img", "startFrame": 1, "endFrame": 713, "nz": 4, "ext": "jpg", "anno_path": "Human5/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Human6", "path": "Human6/img", "startFrame": 1, "endFrame": 792, "nz": 4, "ext": "jpg", "anno_path": "Human6/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Human7", "path": "Human7/img", "startFrame": 1, "endFrame": 250, "nz": 4, "ext": "jpg", "anno_path": "Human7/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Human8", "path": "Human8/img", "startFrame": 1, "endFrame": 128, "nz": 4, "ext": "jpg", "anno_path": "Human8/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Human9", "path": "Human9/img", "startFrame": 1, "endFrame": 305, "nz": 4, "ext": "jpg", "anno_path": "Human9/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Ironman", "path": "Ironman/img", "startFrame": 1, "endFrame": 166, "nz": 4, "ext": "jpg", "anno_path": "Ironman/groundtruth_rect.txt",
"object_class": "person head"},
{"name": "Jogging", "path": "Jogging/img", "startFrame": 1, "endFrame": 307, "nz": 4, "ext": "jpg", "anno_path": "Jogging/groundtruth_rect.1.txt",
"object_class": "person"},
# {"name": "Jogging_1", "path": "Jogging/img", "startFrame": 1, "endFrame": 307, "nz": 4, "ext": "jpg", "anno_path": "Jogging/groundtruth_rect.1.txt",
# "object_class": "person"},
# {"name": "Jogging_2", "path": "Jogging/img", "startFrame": 1, "endFrame": 307, "nz": 4, "ext": "jpg", "anno_path": "Jogging/groundtruth_rect.2.txt",
# "object_class": "person"},
{"name": "Jump", "path": "Jump/img", "startFrame": 1, "endFrame": 122, "nz": 4, "ext": "jpg", "anno_path": "Jump/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Jumping", "path": "Jumping/img", "startFrame": 1, "endFrame": 313, "nz": 4, "ext": "jpg", "anno_path": "Jumping/groundtruth_rect.txt",
"object_class": "face"},
{"name": "KiteSurf", "path": "KiteSurf/img", "startFrame": 1, "endFrame": 84, "nz": 4, "ext": "jpg", "anno_path": "KiteSurf/groundtruth_rect.txt",
"object_class": "face"},
{"name": "Lemming", "path": "Lemming/img", "startFrame": 1, "endFrame": 1336, "nz": 4, "ext": "jpg", "anno_path": "Lemming/groundtruth_rect.txt",
"object_class": "other"},
{"name": "Liquor", "path": "Liquor/img", "startFrame": 1, "endFrame": 1741, "nz": 4, "ext": "jpg", "anno_path": "Liquor/groundtruth_rect.txt",
"object_class": "other"},
{"name": "Man", "path": "Man/img", "startFrame": 1, "endFrame": 134, "nz": 4, "ext": "jpg", "anno_path": "Man/groundtruth_rect.txt",
"object_class": "face"},
{"name": "Matrix", "path": "Matrix/img", "startFrame": 1, "endFrame": 100, "nz": 4, "ext": "jpg", "anno_path": "Matrix/groundtruth_rect.txt",
"object_class": "person head"},
{"name": "Mhyang", "path": "Mhyang/img", "startFrame": 1, "endFrame": 1490, "nz": 4, "ext": "jpg", "anno_path": "Mhyang/groundtruth_rect.txt",
"object_class": "face"},
{"name": "MotorRolling", "path": "MotorRolling/img", "startFrame": 1, "endFrame": 164, "nz": 4, "ext": "jpg", "anno_path": "MotorRolling/groundtruth_rect.txt",
"object_class": "vehicle"},
{"name": "MountainBike", "path": "MountainBike/img", "startFrame": 1, "endFrame": 228, "nz": 4, "ext": "jpg", "anno_path": "MountainBike/groundtruth_rect.txt",
"object_class": "bicycle"},
{"name": "Panda", "path": "Panda/img", "startFrame": 1, "endFrame": 1000, "nz": 4, "ext": "jpg", "anno_path": "Panda/groundtruth_rect.txt",
"object_class": "mammal"},
{"name": "RedTeam", "path": "RedTeam/img", "startFrame": 1, "endFrame": 1918, "nz": 4, "ext": "jpg", "anno_path": "RedTeam/groundtruth_rect.txt",
"object_class": "vehicle"},
{"name": "Rubik", "path": "Rubik/img", "startFrame": 1, "endFrame": 1997, "nz": 4, "ext": "jpg", "anno_path": "Rubik/groundtruth_rect.txt",
"object_class": "other"},
{"name": "Shaking", "path": "Shaking/img", "startFrame": 1, "endFrame": 365, "nz": 4, "ext": "jpg", "anno_path": "Shaking/groundtruth_rect.txt",
"object_class": "face"},
{"name": "Singer1", "path": "Singer1/img", "startFrame": 1, "endFrame": 351, "nz": 4, "ext": "jpg", "anno_path": "Singer1/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Singer2", "path": "Singer2/img", "startFrame": 1, "endFrame": 366, "nz": 4, "ext": "jpg", "anno_path": "Singer2/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Skater", "path": "Skater/img", "startFrame": 1, "endFrame": 160, "nz": 4, "ext": "jpg", "anno_path": "Skater/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Skater2", "path": "Skater2/img", "startFrame": 1, "endFrame": 435, "nz": 4, "ext": "jpg", "anno_path": "Skater2/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Skating1", "path": "Skating1/img", "startFrame": 1, "endFrame": 400, "nz": 4, "ext": "jpg", "anno_path": "Skating1/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Skating2", "path": "Skating2/img", "startFrame": 1, "endFrame": 473, "nz": 4, "ext": "jpg", "anno_path": "Skating2/groundtruth_rect.1.txt",
"object_class": "person"},
{"name": "Skating2_1", "path": "Skating2/img", "startFrame": 1, "endFrame": 473, "nz": 4, "ext": "jpg", "anno_path": "Skating2/groundtruth_rect.1.txt",
"object_class": "person"},
{"name": "Skating2_2", "path": "Skating2/img", "startFrame": 1, "endFrame": 473, "nz": 4, "ext": "jpg", "anno_path": "Skating2/groundtruth_rect.2.txt",
"object_class": "person"},
{"name": "Skiing", "path": "Skiing/img", "startFrame": 1, "endFrame": 81, "nz": 4, "ext": "jpg", "anno_path": "Skiing/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Soccer", "path": "Soccer/img", "startFrame": 1, "endFrame": 392, "nz": 4, "ext": "jpg", "anno_path": "Soccer/groundtruth_rect.txt",
"object_class": "face"},
{"name": "Subway", "path": "Subway/img", "startFrame": 1, "endFrame": 175, "nz": 4, "ext": "jpg", "anno_path": "Subway/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Surfer", "path": "Surfer/img", "startFrame": 1, "endFrame": 376, "nz": 4, "ext": "jpg", "anno_path": "Surfer/groundtruth_rect.txt",
"object_class": "person head"},
{"name": "Suv", "path": "Suv/img", "startFrame": 1, "endFrame": 945, "nz": 4, "ext": "jpg", "anno_path": "Suv/groundtruth_rect.txt",
"object_class": "car"},
{"name": "Sylvester", "path": "Sylvester/img", "startFrame": 1, "endFrame": 1345, "nz": 4, "ext": "jpg", "anno_path": "Sylvester/groundtruth_rect.txt",
"object_class": "other"},
{"name": "Tiger1", "path": "Tiger1/img", "startFrame": 1, "endFrame": 354, "nz": 4, "ext": "jpg", "anno_path": "Tiger1/groundtruth_rect.txt", "initOmit": 5,
"object_class": "other"},
{"name": "Tiger2", "path": "Tiger2/img", "startFrame": 1, "endFrame": 365, "nz": 4, "ext": "jpg", "anno_path": "Tiger2/groundtruth_rect.txt",
"object_class": "other"},
{"name": "Toy", "path": "Toy/img", "startFrame": 1, "endFrame": 271, "nz": 4, "ext": "jpg", "anno_path": "Toy/groundtruth_rect.txt",
"object_class": "other"},
{"name": "Trans", "path": "Trans/img", "startFrame": 1, "endFrame": 124, "nz": 4, "ext": "jpg", "anno_path": "Trans/groundtruth_rect.txt",
"object_class": "other"},
{"name": "Trellis", "path": "Trellis/img", "startFrame": 1, "endFrame": 569, "nz": 4, "ext": "jpg", "anno_path": "Trellis/groundtruth_rect.txt",
"object_class": "face"},
{"name": "Twinnings", "path": "Twinnings/img", "startFrame": 1, "endFrame": 472, "nz": 4, "ext": "jpg", "anno_path": "Twinnings/groundtruth_rect.txt",
"object_class": "other"},
{"name": "Vase", "path": "Vase/img", "startFrame": 1, "endFrame": 271, "nz": 4, "ext": "jpg", "anno_path": "Vase/groundtruth_rect.txt",
"object_class": "other"},
{"name": "Walking", "path": "Walking/img", "startFrame": 1, "endFrame": 412, "nz": 4, "ext": "jpg", "anno_path": "Walking/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Walking2", "path": "Walking2/img", "startFrame": 1, "endFrame": 500, "nz": 4, "ext": "jpg", "anno_path": "Walking2/groundtruth_rect.txt",
"object_class": "person"},
{"name": "Woman", "path": "Woman/img", "startFrame": 1, "endFrame": 597, "nz": 4, "ext": "jpg", "anno_path": "Woman/groundtruth_rect.txt",
"object_class": "person"}
]
return sequence_info_list
\ No newline at end of file
lib/test/evaluation/running.py 0 → 100644
View file @ 75d93027
import numpy as np
import multiprocessing
import os
import sys
from itertools import product
from collections import OrderedDict
from lib.test.evaluation import Sequence, Tracker
import torch
def _save_tracker_output(seq: Sequence, tracker: Tracker, output: dict):
"""Saves the output of the tracker."""
if not os.path.exists(tracker.results_dir):
print("create tracking result dir:", tracker.results_dir)
os.makedirs(tracker.results_dir)
if seq.dataset in ['trackingnet', 'got10k']:
if not os.path.exists(os.path.join(tracker.results_dir, seq.dataset)):
os.makedirs(os.path.join(tracker.results_dir, seq.dataset))
'''2021.1.5 create new folder for these two datasets'''
if seq.dataset in ['trackingnet', 'got10k']:
base_results_path = os.path.join(tracker.results_dir, seq.dataset, seq.name)
else:
base_results_path = os.path.join(tracker.results_dir, seq.name)
def save_bb(file, data):
tracked_bb = np.array(data).astype(int)
np.savetxt(file, tracked_bb, delimiter='\t', fmt='%d')
def save_time(file, data):
exec_times = np.array(data).astype(float)
np.savetxt(file, exec_times, delimiter='\t', fmt='%f')
def save_score(file, data):
scores = np.array(data).astype(float)
np.savetxt(file, scores, delimiter='\t', fmt='%.2f')
def _convert_dict(input_dict):
data_dict = {}
for elem in input_dict:
for k, v in elem.items():
if k in data_dict.keys():
data_dict[k].append(v)
else:
data_dict[k] = [v, ]
return data_dict
for key, data in output.items():
# If data is empty
if not data:
continue
if key == 'target_bbox':
if isinstance(data[0], (dict, OrderedDict)):
data_dict = _convert_dict(data)
for obj_id, d in data_dict.items():
bbox_file = '{}_{}.txt'.format(base_results_path, obj_id)
save_bb(bbox_file, d)
else:
# Single-object mode
bbox_file = '{}.txt'.format(base_results_path)
save_bb(bbox_file, data)
if key == 'all_boxes':
if isinstance(data[0], (dict, OrderedDict)):
data_dict = _convert_dict(data)
for obj_id, d in data_dict.items():
bbox_file = '{}_{}_all_boxes.txt'.format(base_results_path, obj_id)
save_bb(bbox_file, d)
else:
# Single-object mode
bbox_file = '{}_all_boxes.txt'.format(base_results_path)
save_bb(bbox_file, data)
if key == 'all_scores':
if isinstance(data[0], (dict, OrderedDict)):
data_dict = _convert_dict(data)
for obj_id, d in data_dict.items():
bbox_file = '{}_{}_all_scores.txt'.format(base_results_path, obj_id)
save_score(bbox_file, d)
else:
# Single-object mode
print("saving scores...")
bbox_file = '{}_all_scores.txt'.format(base_results_path)
save_score(bbox_file, data)
elif key == 'time':
if isinstance(data[0], dict):
data_dict = _convert_dict(data)
for obj_id, d in data_dict.items():
timings_file = '{}_{}_time.txt'.format(base_results_path, obj_id)
save_time(timings_file, d)
else:
timings_file = '{}_time.txt'.format(base_results_path)
save_time(timings_file, data)
def run_sequence(seq: Sequence, tracker: Tracker, debug=False, num_gpu=8):
"""Runs a tracker on a sequence."""
'''2021.1.2 Add multiple gpu support'''
try:
worker_name = multiprocessing.current_process().name
worker_id = int(worker_name[worker_name.find('-') + 1:]) - 1
gpu_id = worker_id % num_gpu
torch.cuda.set_device(gpu_id)
except:
pass
def _results_exist():
if seq.object_ids is None:
if seq.dataset in ['trackingnet', 'got10k']:
base_results_path = os.path.join(tracker.results_dir, seq.dataset, seq.name)
bbox_file = '{}.txt'.format(base_results_path)
else:
bbox_file = '{}/{}.txt'.format(tracker.results_dir, seq.name)
return os.path.isfile(bbox_file)
else:
bbox_files = ['{}/{}_{}.txt'.format(tracker.results_dir, seq.name, obj_id) for obj_id in seq.object_ids]
missing = [not os.path.isfile(f) for f in bbox_files]
return sum(missing) == 0
if _results_exist() and not debug:
print('FPS: {}'.format(-1))
return
print('Tracker: {} {} {} , Sequence: {}'.format(tracker.name, tracker.parameter_name, tracker.run_id, seq.name))
if debug:
output = tracker.run_sequence(seq, debug=debug)
else:
try:
output = tracker.run_sequence(seq, debug=debug)
except Exception as e:
print(e)
return
sys.stdout.flush()
if isinstance(output['time'][0], (dict, OrderedDict)):
exec_time = sum([sum(times.values()) for times in output['time']])
num_frames = len(output['time'])
else:
exec_time = sum(output['time'])
num_frames = len(output['time'])
print('FPS: {}'.format(num_frames / exec_time))
if not debug:
_save_tracker_output(seq, tracker, output)
def run_dataset(dataset, trackers, debug=False, threads=0, num_gpus=8):
"""Runs a list of trackers on a dataset.
args:
dataset: List of Sequence instances, forming a dataset.
trackers: List of Tracker instances.
debug: Debug level.
threads: Number of threads to use (default 0).
"""
multiprocessing.set_start_method('spawn', force=True)
print('Evaluating {:4d} trackers on {:5d} sequences'.format(len(trackers), len(dataset)))
multiprocessing.set_start_method('spawn', force=True)
if threads == 0:
mode = 'sequential'
else:
mode = 'parallel'
if mode == 'sequential':
for seq in dataset:
for tracker_info in trackers:
run_sequence(seq, tracker_info, debug=debug)
elif mode == 'parallel':
param_list = [(seq, tracker_info, debug, num_gpus) for seq, tracker_info in product(dataset, trackers)]
with multiprocessing.Pool(processes=threads) as pool:
pool.starmap(run_sequence, param_list)
print('Done')
lib/test/evaluation/tc128cedataset.py 0 → 100644
View file @ 75d93027
import numpy as np
from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList
import os
import glob
import six
class TC128CEDataset(BaseDataset):
"""
TC-128 Dataset (78 newly added sequences)
modified from the implementation in got10k-toolkit (https://github.com/got-10k/toolkit)
"""
def __init__(self):
super().__init__()
self.base_path = self.env_settings.tc128_path
self.anno_files = sorted(glob.glob(
os.path.join(self.base_path, '*/*_gt.txt')))
"""filter the newly added sequences (_ce)"""
self.anno_files = [s for s in self.anno_files if "_ce" in s]
self.seq_dirs = [os.path.dirname(f) for f in self.anno_files]
self.seq_names = [os.path.basename(d) for d in self.seq_dirs]
# valid frame range for each sequence
self.range_files = [glob.glob(os.path.join(d, '*_frames.txt'))[0] for d in self.seq_dirs]
def get_sequence_list(self):
return SequenceList([self._construct_sequence(s) for s in self.seq_names])
def _construct_sequence(self, sequence_name):
if isinstance(sequence_name, six.string_types):
if not sequence_name in self.seq_names:
raise Exception('Sequence {} not found.'.format(sequence_name))
index = self.seq_names.index(sequence_name)
# load valid frame range
frames = np.loadtxt(self.range_files[index], dtype=int, delimiter=',')
img_files = [os.path.join(self.seq_dirs[index], 'img/%04d.jpg' % f) for f in range(frames[0], frames[1] + 1)]
# load annotations
anno = np.loadtxt(self.anno_files[index], delimiter=',')
assert len(img_files) == len(anno)
assert anno.shape[1] == 4
# return img_files, anno
return Sequence(sequence_name, img_files, 'tc128', anno.reshape(-1, 4))
def __len__(self):
return len(self.seq_names)
lib/test/evaluation/tc128dataset.py 0 → 100644
View file @ 75d93027
import numpy as np
from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList
import os
import glob
import six
class TC128Dataset(BaseDataset):
"""
TC-128 Dataset
modified from the implementation in got10k-toolkit (https://github.com/got-10k/toolkit)
"""
def __init__(self):
super().__init__()
self.base_path = self.env_settings.tc128_path
self.anno_files = sorted(glob.glob(
os.path.join(self.base_path, '*/*_gt.txt')))
self.seq_dirs = [os.path.dirname(f) for f in self.anno_files]
self.seq_names = [os.path.basename(d) for d in self.seq_dirs]
# valid frame range for each sequence
self.range_files = [glob.glob(os.path.join(d, '*_frames.txt'))[0] for d in self.seq_dirs]
def get_sequence_list(self):
return SequenceList([self._construct_sequence(s) for s in self.seq_names])
def _construct_sequence(self, sequence_name):
if isinstance(sequence_name, six.string_types):
if not sequence_name in self.seq_names:
raise Exception('Sequence {} not found.'.format(sequence_name))
index = self.seq_names.index(sequence_name)
# load valid frame range
frames = np.loadtxt(self.range_files[index], dtype=int, delimiter=',')
img_files = [os.path.join(self.seq_dirs[index], 'img/%04d.jpg' % f) for f in range(frames[0], frames[1] + 1)]
# load annotations
anno = np.loadtxt(self.anno_files[index], delimiter=',')
assert len(img_files) == len(anno)
assert anno.shape[1] == 4
# return img_files, anno
return Sequence(sequence_name, img_files, 'tc128', anno.reshape(-1, 4))
def __len__(self):
return len(self.seq_names)
lib/test/evaluation/tnl2kdataset.py 0 → 100644
View file @ 75d93027
import os
import numpy as np
from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList
from lib.test.utils.load_text import load_text, load_str
############
# current 00000492.png of test_015_Sord_video_Q01_done is damaged and replaced by a copy of 00000491.png
############
class TNL2kDataset(BaseDataset):
"""
TNL2k test set
"""
def __init__(self):
super().__init__()
self.base_path = self.env_settings.tnl2k_path
self.sequence_list = self._get_sequence_list()
def get_sequence_list(self):
return SequenceList([self._construct_sequence(s) for s in self.sequence_list])
def _construct_sequence(self, sequence_name):
# class_name = sequence_name.split('-')[0]
anno_path = '{}/{}/groundtruth.txt'.format(self.base_path, sequence_name)
ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64)
text_dsp_path = '{}/{}/language.txt'.format(self.base_path, sequence_name)
text_dsp = load_str(text_dsp_path)
frames_path = '{}/{}/imgs'.format(self.base_path, sequence_name)
frames_list = [f for f in os.listdir(frames_path)]
frames_list = sorted(frames_list)
frames_list = ['{}/{}'.format(frames_path, frame_i) for frame_i in frames_list]
# target_class = class_name
return Sequence(sequence_name, frames_list, 'tnl2k', ground_truth_rect.reshape(-1, 4), text_dsp=text_dsp)
def __len__(self):
return len(self.sequence_list)
def _get_sequence_list(self):
sequence_list = []
for seq in os.listdir(self.base_path):
if os.path.isdir(os.path.join(self.base_path, seq)):
sequence_list.append(seq)
return sequence_list
lib/test/evaluation/tracker.py 0 → 100644
View file @ 75d93027
import importlib
import os
from collections import OrderedDict
from lib.test.evaluation.environment import env_settings
import time
import cv2 as cv
from lib.utils.lmdb_utils import decode_img
from pathlib import Path
import numpy as np
def trackerlist(name: str, parameter_name: str, dataset_name: str, run_ids = None, display_name: str = None,
result_only=False):
"""Generate list of trackers.
args:
name: Name of tracking method.
parameter_name: Name of parameter file.
run_ids: A single or list of run_ids.
display_name: Name to be displayed in the result plots.
"""
if run_ids is None or isinstance(run_ids, int):
run_ids = [run_ids]
return [Tracker(name, parameter_name, dataset_name, run_id, display_name, result_only) for run_id in run_ids]
class Tracker:
"""Wraps the tracker for evaluation and running purposes.
args:
name: Name of tracking method.
parameter_name: Name of parameter file.
run_id: The run id.
display_name: Name to be displayed in the result plots.
"""
def __init__(self, name: str, parameter_name: str, dataset_name: str, run_id: int = None, display_name: str = None,
result_only=False):
assert run_id is None or isinstance(run_id, int)
self.name = name
self.parameter_name = parameter_name
self.dataset_name = dataset_name
self.run_id = run_id
self.display_name = display_name
env = env_settings()
if self.run_id is None:
self.results_dir = '{}/{}/{}'.format(env.results_path, self.name, self.parameter_name)
else:
self.results_dir = '{}/{}/{}_{:03d}'.format(env.results_path, self.name, self.parameter_name, self.run_id)
if result_only:
self.results_dir = '{}/{}'.format(env.results_path, self.name)
tracker_module_abspath = os.path.abspath(os.path.join(os.path.dirname(__file__),
'..', 'tracker', '%s.py' % self.name))
if os.path.isfile(tracker_module_abspath):
tracker_module = importlib.import_module('lib.test.tracker.{}'.format(self.name))
self.tracker_class = tracker_module.get_tracker_class()
else:
self.tracker_class = None
def create_tracker(self, params):
tracker = self.tracker_class(params, self.dataset_name)
return tracker
def run_sequence(self, seq, debug=None):
"""Run tracker on sequence.
args:
seq: Sequence to run the tracker on.
visualization: Set visualization flag (None means default value specified in the parameters).
debug: Set debug level (None means default value specified in the parameters).
multiobj_mode: Which mode to use for multiple objects.
"""
params = self.get_parameters()
debug_ = debug
if debug is None:
debug_ = getattr(params, 'debug', 0)
params.debug = debug_
# Get init information
init_info = seq.init_info()
tracker = self.create_tracker(params)
output = self._track_sequence(tracker, seq, init_info)
return output
def _track_sequence(self, tracker, seq, init_info):
# Define outputs
# Each field in output is a list containing tracker prediction for each frame.
# In case of single object tracking mode:
# target_bbox[i] is the predicted bounding box for frame i
# time[i] is the processing time for frame i
# In case of multi object tracking mode:
# target_bbox[i] is an OrderedDict, where target_bbox[i][obj_id] is the predicted box for target obj_id in
# frame i
# time[i] is either the processing time for frame i, or an OrderedDict containing processing times for each
# object in frame i
output = {'target_bbox': [],
'time': []}
if tracker.params.save_all_boxes:
output['all_boxes'] = []
output['all_scores'] = []
def _store_outputs(tracker_out: dict, defaults=None):
defaults = {} if defaults is None else defaults
for key in output.keys():
val = tracker_out.get(key, defaults.get(key, None))
if key in tracker_out or val is not None:
output[key].append(val)
# Initialize
image = self._read_image(seq.frames[0])
start_time = time.time()
out = tracker.initialize(image, init_info)
if out is None:
out = {}
prev_output = OrderedDict(out)
init_default = {'target_bbox': init_info.get('init_bbox'),
'time': time.time() - start_time}
if tracker.params.save_all_boxes:
init_default['all_boxes'] = out['all_boxes']
init_default['all_scores'] = out['all_scores']
_store_outputs(out, init_default)
for frame_num, frame_path in enumerate(seq.frames[1:], start=1):
image = self._read_image(frame_path)
start_time = time.time()
info = seq.frame_info(frame_num)
info['previous_output'] = prev_output
if len(seq.ground_truth_rect) > 1:
info['gt_bbox'] = seq.ground_truth_rect[frame_num]
out = tracker.track(image, info)
prev_output = OrderedDict(out)
_store_outputs(out, {'time': time.time() - start_time})
for key in ['target_bbox', 'all_boxes', 'all_scores']:
if key in output and len(output[key]) <= 1:
output.pop(key)
return output
def run_video(self, videofilepath, optional_box=None, debug=None, visdom_info=None, save_results=False):
"""Run the tracker with the vieofile.
args:
debug: Debug level.
"""
params = self.get_parameters()
debug_ = debug
if debug is None:
debug_ = getattr(params, 'debug', 0)
params.debug = debug_
params.tracker_name = self.name
params.param_name = self.parameter_name
# self._init_visdom(visdom_info, debug_)
multiobj_mode = getattr(params, 'multiobj_mode', getattr(self.tracker_class, 'multiobj_mode', 'default'))
if multiobj_mode == 'default':
tracker = self.create_tracker(params)
elif multiobj_mode == 'parallel':
tracker = MultiObjectWrapper(self.tracker_class, params, self.visdom, fast_load=True)
else:
raise ValueError('Unknown multi object mode {}'.format(multiobj_mode))
assert os.path.isfile(videofilepath), "Invalid param {}".format(videofilepath)
", videofilepath must be a valid videofile"
output_boxes = []
cap = cv.VideoCapture(videofilepath)
display_name = 'Display: ' + tracker.params.tracker_name
cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO)
cv.resizeWindow(display_name, 960, 720)
success, frame = cap.read()
cv.imshow(display_name, frame)
def _build_init_info(box):
return {'init_bbox': box}
if success is not True:
print("Read frame from {} failed.".format(videofilepath))
exit(-1)
if optional_box is not None:
assert isinstance(optional_box, (list, tuple))
assert len(optional_box) == 4, "valid box's foramt is [x,y,w,h]"
tracker.initialize(frame, _build_init_info(optional_box))
output_boxes.append(optional_box)
else:
while True:
# cv.waitKey()
frame_disp = frame.copy()
cv.putText(frame_disp, 'Select target ROI and press ENTER', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL,
1.5, (0, 0, 0), 1)
x, y, w, h = cv.selectROI(display_name, frame_disp, fromCenter=False)
init_state = [x, y, w, h]
tracker.initialize(frame, _build_init_info(init_state))
output_boxes.append(init_state)
break
while True:
ret, frame = cap.read()
if frame is None:
break
frame_disp = frame.copy()
# Draw box
out = tracker.track(frame)
state = [int(s) for s in out['target_bbox']]
output_boxes.append(state)
cv.rectangle(frame_disp, (state[0], state[1]), (state[2] + state[0], state[3] + state[1]),
(0, 255, 0), 5)
font_color = (0, 0, 0)
cv.putText(frame_disp, 'Tracking!', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1,
font_color, 1)
cv.putText(frame_disp, 'Press r to reset', (20, 55), cv.FONT_HERSHEY_COMPLEX_SMALL, 1,
font_color, 1)
cv.putText(frame_disp, 'Press q to quit', (20, 80), cv.FONT_HERSHEY_COMPLEX_SMALL, 1,
font_color, 1)
# Display the resulting frame
cv.imshow(display_name, frame_disp)
key = cv.waitKey(1)
if key == ord('q'):
break
elif key == ord('r'):
ret, frame = cap.read()
frame_disp = frame.copy()
cv.putText(frame_disp, 'Select target ROI and press ENTER', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1.5,
(0, 0, 0), 1)
cv.imshow(display_name, frame_disp)
x, y, w, h = cv.selectROI(display_name, frame_disp, fromCenter=False)
init_state = [x, y, w, h]
tracker.initialize(frame, _build_init_info(init_state))
output_boxes.append(init_state)
# When everything done, release the capture
cap.release()
cv.destroyAllWindows()
if save_results:
if not os.path.exists(self.results_dir):
os.makedirs(self.results_dir)
video_name = Path(videofilepath).stem
base_results_path = os.path.join(self.results_dir, 'video_{}'.format(video_name))
tracked_bb = np.array(output_boxes).astype(int)
bbox_file = '{}.txt'.format(base_results_path)
np.savetxt(bbox_file, tracked_bb, delimiter='\t', fmt='%d')
def get_parameters(self):
"""Get parameters."""
param_module = importlib.import_module('lib.test.parameter.{}'.format(self.name))
params = param_module.parameters(self.parameter_name)
return params
def _read_image(self, image_file: str):
if isinstance(image_file, str):
im = cv.imread(image_file)
return cv.cvtColor(im, cv.COLOR_BGR2RGB)
elif isinstance(image_file, list) and len(image_file) == 2:
return decode_img(image_file[0], image_file[1])
else:
raise ValueError("type of image_file should be str or list")
lib/test/evaluation/trackingnetdataset.py 0 → 100644
View file @ 75d93027
import numpy as np
from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList
import os
from lib.test.utils.load_text import load_text
class TrackingNetDataset(BaseDataset):
""" TrackingNet test set.
Publication:
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild.
Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem
ECCV, 2018
https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf
Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit.
"""
def __init__(self):
super().__init__()
self.base_path = self.env_settings.trackingnet_path
sets = 'TEST'
if not isinstance(sets, (list, tuple)):
if sets == 'TEST':
sets = ['TEST']
elif sets == 'TRAIN':
sets = ['TRAIN_{}'.format(i) for i in range(5)]
self.sequence_list = self._list_sequences(self.base_path, sets)
def get_sequence_list(self):
return SequenceList([self._construct_sequence(set, seq_name) for set, seq_name in self.sequence_list])
def _construct_sequence(self, set, sequence_name):
anno_path = '{}/{}/anno/{}.txt'.format(self.base_path, set, sequence_name)
ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64, backend='numpy')
frames_path = '{}/{}/frames/{}'.format(self.base_path, set, sequence_name)
frame_list = [frame for frame in os.listdir(frames_path) if frame.endswith(".jpg")]
frame_list.sort(key=lambda f: int(f[:-4]))
frames_list = [os.path.join(frames_path, frame) for frame in frame_list]
return Sequence(sequence_name, frames_list, 'trackingnet', ground_truth_rect.reshape(-1, 4))
def __len__(self):
return len(self.sequence_list)
def _list_sequences(self, root, set_ids):
sequence_list = []
for s in set_ids:
anno_dir = os.path.join(root, s, "anno")
sequences_cur_set = [(s, os.path.splitext(f)[0]) for f in os.listdir(anno_dir) if f.endswith('.txt')]
sequence_list += sequences_cur_set
return sequence_list
lib/test/evaluation/uavdataset.py 0 → 100644
View file @ 75d93027
import numpy as np
from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList
from lib.test.utils.load_text import load_text
class UAVDataset(BaseDataset):
""" UAV123 dataset.
Publication:
A Benchmark and Simulator for UAV Tracking.
Matthias Mueller, Neil Smith and Bernard Ghanem
ECCV, 2016
https://ivul.kaust.edu.sa/Documents/Publications/2016/A%20Benchmark%20and%20Simulator%20for%20UAV%20Tracking.pdf
Download the dataset from https://ivul.kaust.edu.sa/Pages/pub-benchmark-simulator-uav.aspx
"""
def __init__(self):
super().__init__()
self.base_path = self.env_settings.uav_path
self.sequence_info_list = self._get_sequence_info_list()
def get_sequence_list(self):
# return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list])
return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list])
def _construct_sequence(self, sequence_info):
sequence_path = sequence_info['path']
nz = sequence_info['nz']
ext = sequence_info['ext']
start_frame = sequence_info['startFrame']
end_frame = sequence_info['endFrame']
init_omit = 0
if 'initOmit' in sequence_info:
init_omit = sequence_info['initOmit']
frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path,
sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext) for frame_num in range(start_frame+init_omit, end_frame+1)]
anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path'])
ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64, backend='numpy')
return Sequence(sequence_info['name'][4:], frames, 'uav', ground_truth_rect[init_omit:,:],
object_class=sequence_info['object_class'])
def __len__(self):
return len(self.sequence_info_list)
def _get_sequence_info_list(self):
sequence_info_list = [
{"name": "uav_bike1", "path": "data_seq/UAV123/bike1", "startFrame": 1, "endFrame": 3085, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/bike1.txt", "object_class": "vehicle"},
{"name": "uav_bike2", "path": "data_seq/UAV123/bike2", "startFrame": 1, "endFrame": 553, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/bike2.txt", "object_class": "vehicle"},
{"name": "uav_bike3", "path": "data_seq/UAV123/bike3", "startFrame": 1, "endFrame": 433, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/bike3.txt", "object_class": "vehicle"},
{"name": "uav_bird1_1", "path": "data_seq/UAV123/bird1", "startFrame": 1, "endFrame": 253, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/bird1_1.txt", "object_class": "bird"},
{"name": "uav_bird1_2", "path": "data_seq/UAV123/bird1", "startFrame": 775, "endFrame": 1477, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/bird1_2.txt", "object_class": "bird"},
{"name": "uav_bird1_3", "path": "data_seq/UAV123/bird1", "startFrame": 1573, "endFrame": 2437, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/bird1_3.txt", "object_class": "bird"},
{"name": "uav_boat1", "path": "data_seq/UAV123/boat1", "startFrame": 1, "endFrame": 901, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/boat1.txt", "object_class": "vessel"},
{"name": "uav_boat2", "path": "data_seq/UAV123/boat2", "startFrame": 1, "endFrame": 799, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/boat2.txt", "object_class": "vessel"},
{"name": "uav_boat3", "path": "data_seq/UAV123/boat3", "startFrame": 1, "endFrame": 901, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/boat3.txt", "object_class": "vessel"},
{"name": "uav_boat4", "path": "data_seq/UAV123/boat4", "startFrame": 1, "endFrame": 553, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/boat4.txt", "object_class": "vessel"},
{"name": "uav_boat5", "path": "data_seq/UAV123/boat5", "startFrame": 1, "endFrame": 505, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/boat5.txt", "object_class": "vessel"},
{"name": "uav_boat6", "path": "data_seq/UAV123/boat6", "startFrame": 1, "endFrame": 805, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/boat6.txt", "object_class": "vessel"},
{"name": "uav_boat7", "path": "data_seq/UAV123/boat7", "startFrame": 1, "endFrame": 535, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/boat7.txt", "object_class": "vessel"},
{"name": "uav_boat8", "path": "data_seq/UAV123/boat8", "startFrame": 1, "endFrame": 685, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/boat8.txt", "object_class": "vessel"},
{"name": "uav_boat9", "path": "data_seq/UAV123/boat9", "startFrame": 1, "endFrame": 1399, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/boat9.txt", "object_class": "vessel"},
{"name": "uav_building1", "path": "data_seq/UAV123/building1", "startFrame": 1, "endFrame": 469, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/building1.txt", "object_class": "other"},
{"name": "uav_building2", "path": "data_seq/UAV123/building2", "startFrame": 1, "endFrame": 577, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/building2.txt", "object_class": "other"},
{"name": "uav_building3", "path": "data_seq/UAV123/building3", "startFrame": 1, "endFrame": 829, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/building3.txt", "object_class": "other"},
{"name": "uav_building4", "path": "data_seq/UAV123/building4", "startFrame": 1, "endFrame": 787, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/building4.txt", "object_class": "other"},
{"name": "uav_building5", "path": "data_seq/UAV123/building5", "startFrame": 1, "endFrame": 481, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/building5.txt", "object_class": "other"},
{"name": "uav_car1_1", "path": "data_seq/UAV123/car1", "startFrame": 1, "endFrame": 751, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car1_1.txt", "object_class": "car"},
{"name": "uav_car1_2", "path": "data_seq/UAV123/car1", "startFrame": 751, "endFrame": 1627, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car1_2.txt", "object_class": "car"},
{"name": "uav_car1_3", "path": "data_seq/UAV123/car1", "startFrame": 1627, "endFrame": 2629, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car1_3.txt", "object_class": "car"},
{"name": "uav_car10", "path": "data_seq/UAV123/car10", "startFrame": 1, "endFrame": 1405, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car10.txt", "object_class": "car"},
{"name": "uav_car11", "path": "data_seq/UAV123/car11", "startFrame": 1, "endFrame": 337, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car11.txt", "object_class": "car"},
{"name": "uav_car12", "path": "data_seq/UAV123/car12", "startFrame": 1, "endFrame": 499, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car12.txt", "object_class": "car"},
{"name": "uav_car13", "path": "data_seq/UAV123/car13", "startFrame": 1, "endFrame": 415, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car13.txt", "object_class": "car"},
{"name": "uav_car14", "path": "data_seq/UAV123/car14", "startFrame": 1, "endFrame": 1327, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car14.txt", "object_class": "car"},
{"name": "uav_car15", "path": "data_seq/UAV123/car15", "startFrame": 1, "endFrame": 469, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car15.txt", "object_class": "car"},
{"name": "uav_car16_1", "path": "data_seq/UAV123/car16", "startFrame": 1, "endFrame": 415, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car16_1.txt", "object_class": "car"},
{"name": "uav_car16_2", "path": "data_seq/UAV123/car16", "startFrame": 415, "endFrame": 1993, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car16_2.txt", "object_class": "car"},
{"name": "uav_car17", "path": "data_seq/UAV123/car17", "startFrame": 1, "endFrame": 1057, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car17.txt", "object_class": "car"},
{"name": "uav_car18", "path": "data_seq/UAV123/car18", "startFrame": 1, "endFrame": 1207, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car18.txt", "object_class": "car"},
{"name": "uav_car1_s", "path": "data_seq/UAV123/car1_s", "startFrame": 1, "endFrame": 1475, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car1_s.txt", "object_class": "car"},
{"name": "uav_car2", "path": "data_seq/UAV123/car2", "startFrame": 1, "endFrame": 1321, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car2.txt", "object_class": "car"},
{"name": "uav_car2_s", "path": "data_seq/UAV123/car2_s", "startFrame": 1, "endFrame": 320, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car2_s.txt", "object_class": "car"},
{"name": "uav_car3", "path": "data_seq/UAV123/car3", "startFrame": 1, "endFrame": 1717, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car3.txt", "object_class": "car"},
{"name": "uav_car3_s", "path": "data_seq/UAV123/car3_s", "startFrame": 1, "endFrame": 1300, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car3_s.txt", "object_class": "car"},
{"name": "uav_car4", "path": "data_seq/UAV123/car4", "startFrame": 1, "endFrame": 1345, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car4.txt", "object_class": "car"},
{"name": "uav_car4_s", "path": "data_seq/UAV123/car4_s", "startFrame": 1, "endFrame": 830, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car4_s.txt", "object_class": "car"},
{"name": "uav_car5", "path": "data_seq/UAV123/car5", "startFrame": 1, "endFrame": 745, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car5.txt", "object_class": "car"},
{"name": "uav_car6_1", "path": "data_seq/UAV123/car6", "startFrame": 1, "endFrame": 487, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car6_1.txt", "object_class": "car"},
{"name": "uav_car6_2", "path": "data_seq/UAV123/car6", "startFrame": 487, "endFrame": 1807, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car6_2.txt", "object_class": "car"},
{"name": "uav_car6_3", "path": "data_seq/UAV123/car6", "startFrame": 1807, "endFrame": 2953, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car6_3.txt", "object_class": "car"},
{"name": "uav_car6_4", "path": "data_seq/UAV123/car6", "startFrame": 2953, "endFrame": 3925, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car6_4.txt", "object_class": "car"},
{"name": "uav_car6_5", "path": "data_seq/UAV123/car6", "startFrame": 3925, "endFrame": 4861, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car6_5.txt", "object_class": "car"},
{"name": "uav_car7", "path": "data_seq/UAV123/car7", "startFrame": 1, "endFrame": 1033, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car7.txt", "object_class": "car"},
{"name": "uav_car8_1", "path": "data_seq/UAV123/car8", "startFrame": 1, "endFrame": 1357, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car8_1.txt", "object_class": "car"},
{"name": "uav_car8_2", "path": "data_seq/UAV123/car8", "startFrame": 1357, "endFrame": 2575, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car8_2.txt", "object_class": "car"},
{"name": "uav_car9", "path": "data_seq/UAV123/car9", "startFrame": 1, "endFrame": 1879, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/car9.txt", "object_class": "car"},
{"name": "uav_group1_1", "path": "data_seq/UAV123/group1", "startFrame": 1, "endFrame": 1333, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/group1_1.txt", "object_class": "person"},
{"name": "uav_group1_2", "path": "data_seq/UAV123/group1", "startFrame": 1333, "endFrame": 2515, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/group1_2.txt", "object_class": "person"},
{"name": "uav_group1_3", "path": "data_seq/UAV123/group1", "startFrame": 2515, "endFrame": 3925, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/group1_3.txt", "object_class": "person"},
{"name": "uav_group1_4", "path": "data_seq/UAV123/group1", "startFrame": 3925, "endFrame": 4873, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/group1_4.txt", "object_class": "person"},
{"name": "uav_group2_1", "path": "data_seq/UAV123/group2", "startFrame": 1, "endFrame": 907, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/group2_1.txt", "object_class": "person"},
{"name": "uav_group2_2", "path": "data_seq/UAV123/group2", "startFrame": 907, "endFrame": 1771, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/group2_2.txt", "object_class": "person"},
{"name": "uav_group2_3", "path": "data_seq/UAV123/group2", "startFrame": 1771, "endFrame": 2683, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/group2_3.txt", "object_class": "person"},
{"name": "uav_group3_1", "path": "data_seq/UAV123/group3", "startFrame": 1, "endFrame": 1567, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/group3_1.txt", "object_class": "person"},
{"name": "uav_group3_2", "path": "data_seq/UAV123/group3", "startFrame": 1567, "endFrame": 2827, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/group3_2.txt", "object_class": "person"},
{"name": "uav_group3_3", "path": "data_seq/UAV123/group3", "startFrame": 2827, "endFrame": 4369, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/group3_3.txt", "object_class": "person"},
{"name": "uav_group3_4", "path": "data_seq/UAV123/group3", "startFrame": 4369, "endFrame": 5527, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/group3_4.txt", "object_class": "person"},
{"name": "uav_person1", "path": "data_seq/UAV123/person1", "startFrame": 1, "endFrame": 799, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person1.txt", "object_class": "person"},
{"name": "uav_person10", "path": "data_seq/UAV123/person10", "startFrame": 1, "endFrame": 1021, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person10.txt", "object_class": "person"},
{"name": "uav_person11", "path": "data_seq/UAV123/person11", "startFrame": 1, "endFrame": 721, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person11.txt", "object_class": "person"},
{"name": "uav_person12_1", "path": "data_seq/UAV123/person12", "startFrame": 1, "endFrame": 601, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person12_1.txt", "object_class": "person"},
{"name": "uav_person12_2", "path": "data_seq/UAV123/person12", "startFrame": 601, "endFrame": 1621, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person12_2.txt", "object_class": "person"},
{"name": "uav_person13", "path": "data_seq/UAV123/person13", "startFrame": 1, "endFrame": 883, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person13.txt", "object_class": "person"},
{"name": "uav_person14_1", "path": "data_seq/UAV123/person14", "startFrame": 1, "endFrame": 847, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person14_1.txt", "object_class": "person"},
{"name": "uav_person14_2", "path": "data_seq/UAV123/person14", "startFrame": 847, "endFrame": 1813, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person14_2.txt", "object_class": "person"},
{"name": "uav_person14_3", "path": "data_seq/UAV123/person14", "startFrame": 1813, "endFrame": 2923,
"nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person14_3.txt", "object_class": "person"},
{"name": "uav_person15", "path": "data_seq/UAV123/person15", "startFrame": 1, "endFrame": 1339, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person15.txt", "object_class": "person"},
{"name": "uav_person16", "path": "data_seq/UAV123/person16", "startFrame": 1, "endFrame": 1147, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person16.txt", "object_class": "person"},
{"name": "uav_person17_1", "path": "data_seq/UAV123/person17", "startFrame": 1, "endFrame": 1501, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person17_1.txt", "object_class": "person"},
{"name": "uav_person17_2", "path": "data_seq/UAV123/person17", "startFrame": 1501, "endFrame": 2347,
"nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person17_2.txt", "object_class": "person"},
{"name": "uav_person18", "path": "data_seq/UAV123/person18", "startFrame": 1, "endFrame": 1393, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person18.txt", "object_class": "person"},
{"name": "uav_person19_1", "path": "data_seq/UAV123/person19", "startFrame": 1, "endFrame": 1243, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person19_1.txt", "object_class": "person"},
{"name": "uav_person19_2", "path": "data_seq/UAV123/person19", "startFrame": 1243, "endFrame": 2791,
"nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person19_2.txt", "object_class": "person"},
{"name": "uav_person19_3", "path": "data_seq/UAV123/person19", "startFrame": 2791, "endFrame": 4357,
"nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/person19_3.txt", "object_class": "person"},
{"name": "uav_person1_s", "path": "data_seq/UAV123/person1_s", "startFrame": 1, "endFrame": 1600, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person1_s.txt", "object_class": "person"},
{"name": "uav_person2_1", "path": "data_seq/UAV123/person2", "startFrame": 1, "endFrame": 1189, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person2_1.txt", "object_class": "person"},
{"name": "uav_person2_2", "path": "data_seq/UAV123/person2", "startFrame": 1189, "endFrame": 2623, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person2_2.txt", "object_class": "person"},
{"name": "uav_person20", "path": "data_seq/UAV123/person20", "startFrame": 1, "endFrame": 1783, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person20.txt", "object_class": "person"},
{"name": "uav_person21", "path": "data_seq/UAV123/person21", "startFrame": 1, "endFrame": 487, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person21.txt", "object_class": "person"},
{"name": "uav_person22", "path": "data_seq/UAV123/person22", "startFrame": 1, "endFrame": 199, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person22.txt", "object_class": "person"},
{"name": "uav_person23", "path": "data_seq/UAV123/person23", "startFrame": 1, "endFrame": 397, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person23.txt", "object_class": "person"},
{"name": "uav_person2_s", "path": "data_seq/UAV123/person2_s", "startFrame": 1, "endFrame": 250, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person2_s.txt", "object_class": "person"},
{"name": "uav_person3", "path": "data_seq/UAV123/person3", "startFrame": 1, "endFrame": 643, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person3.txt", "object_class": "person"},
{"name": "uav_person3_s", "path": "data_seq/UAV123/person3_s", "startFrame": 1, "endFrame": 505, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person3_s.txt", "object_class": "person"},
{"name": "uav_person4_1", "path": "data_seq/UAV123/person4", "startFrame": 1, "endFrame": 1501, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person4_1.txt", "object_class": "person"},
{"name": "uav_person4_2", "path": "data_seq/UAV123/person4", "startFrame": 1501, "endFrame": 2743, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person4_2.txt", "object_class": "person"},
{"name": "uav_person5_1", "path": "data_seq/UAV123/person5", "startFrame": 1, "endFrame": 877, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person5_1.txt", "object_class": "person"},
{"name": "uav_person5_2", "path": "data_seq/UAV123/person5", "startFrame": 877, "endFrame": 2101, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person5_2.txt", "object_class": "person"},
{"name": "uav_person6", "path": "data_seq/UAV123/person6", "startFrame": 1, "endFrame": 901, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person6.txt", "object_class": "person"},
{"name": "uav_person7_1", "path": "data_seq/UAV123/person7", "startFrame": 1, "endFrame": 1249, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person7_1.txt", "object_class": "person"},
{"name": "uav_person7_2", "path": "data_seq/UAV123/person7", "startFrame": 1249, "endFrame": 2065, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person7_2.txt", "object_class": "person"},
{"name": "uav_person8_1", "path": "data_seq/UAV123/person8", "startFrame": 1, "endFrame": 1075, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person8_1.txt", "object_class": "person"},
{"name": "uav_person8_2", "path": "data_seq/UAV123/person8", "startFrame": 1075, "endFrame": 1525, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person8_2.txt", "object_class": "person"},
{"name": "uav_person9", "path": "data_seq/UAV123/person9", "startFrame": 1, "endFrame": 661, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/person9.txt", "object_class": "person"},
{"name": "uav_truck1", "path": "data_seq/UAV123/truck1", "startFrame": 1, "endFrame": 463, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/truck1.txt", "object_class": "truck"},
{"name": "uav_truck2", "path": "data_seq/UAV123/truck2", "startFrame": 1, "endFrame": 385, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/truck2.txt", "object_class": "truck"},
{"name": "uav_truck3", "path": "data_seq/UAV123/truck3", "startFrame": 1, "endFrame": 535, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/truck3.txt", "object_class": "truck"},
{"name": "uav_truck4_1", "path": "data_seq/UAV123/truck4", "startFrame": 1, "endFrame": 577, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/truck4_1.txt", "object_class": "truck"},
{"name": "uav_truck4_2", "path": "data_seq/UAV123/truck4", "startFrame": 577, "endFrame": 1261, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/truck4_2.txt", "object_class": "truck"},
{"name": "uav_uav1_1", "path": "data_seq/UAV123/uav1", "startFrame": 1, "endFrame": 1555, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/uav1_1.txt", "object_class": "aircraft"},
{"name": "uav_uav1_2", "path": "data_seq/UAV123/uav1", "startFrame": 1555, "endFrame": 2377, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/uav1_2.txt", "object_class": "aircraft"},
{"name": "uav_uav1_3", "path": "data_seq/UAV123/uav1", "startFrame": 2473, "endFrame": 3469, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/uav1_3.txt", "object_class": "aircraft"},
{"name": "uav_uav2", "path": "data_seq/UAV123/uav2", "startFrame": 1, "endFrame": 133, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/uav2.txt", "object_class": "aircraft"},
{"name": "uav_uav3", "path": "data_seq/UAV123/uav3", "startFrame": 1, "endFrame": 265, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/uav3.txt", "object_class": "aircraft"},
{"name": "uav_uav4", "path": "data_seq/UAV123/uav4", "startFrame": 1, "endFrame": 157, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/uav4.txt", "object_class": "aircraft"},
{"name": "uav_uav5", "path": "data_seq/UAV123/uav5", "startFrame": 1, "endFrame": 139, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/uav5.txt", "object_class": "aircraft"},
{"name": "uav_uav6", "path": "data_seq/UAV123/uav6", "startFrame": 1, "endFrame": 109, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/uav6.txt", "object_class": "aircraft"},
{"name": "uav_uav7", "path": "data_seq/UAV123/uav7", "startFrame": 1, "endFrame": 373, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/uav7.txt", "object_class": "aircraft"},
{"name": "uav_uav8", "path": "data_seq/UAV123/uav8", "startFrame": 1, "endFrame": 301, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/uav8.txt", "object_class": "aircraft"},
{"name": "uav_wakeboard1", "path": "data_seq/UAV123/wakeboard1", "startFrame": 1, "endFrame": 421, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/wakeboard1.txt", "object_class": "person"},
{"name": "uav_wakeboard10", "path": "data_seq/UAV123/wakeboard10", "startFrame": 1, "endFrame": 469,
"nz": 6, "ext": "jpg", "anno_path": "anno/UAV123/wakeboard10.txt", "object_class": "person"},
{"name": "uav_wakeboard2", "path": "data_seq/UAV123/wakeboard2", "startFrame": 1, "endFrame": 733, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/wakeboard2.txt", "object_class": "person"},
{"name": "uav_wakeboard3", "path": "data_seq/UAV123/wakeboard3", "startFrame": 1, "endFrame": 823, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/wakeboard3.txt", "object_class": "person"},
{"name": "uav_wakeboard4", "path": "data_seq/UAV123/wakeboard4", "startFrame": 1, "endFrame": 697, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/wakeboard4.txt", "object_class": "person"},
{"name": "uav_wakeboard5", "path": "data_seq/UAV123/wakeboard5", "startFrame": 1, "endFrame": 1675, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/wakeboard5.txt", "object_class": "person"},
{"name": "uav_wakeboard6", "path": "data_seq/UAV123/wakeboard6", "startFrame": 1, "endFrame": 1165, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/wakeboard6.txt", "object_class": "person"},
{"name": "uav_wakeboard7", "path": "data_seq/UAV123/wakeboard7", "startFrame": 1, "endFrame": 199, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/wakeboard7.txt", "object_class": "person"},
{"name": "uav_wakeboard8", "path": "data_seq/UAV123/wakeboard8", "startFrame": 1, "endFrame": 1543, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/wakeboard8.txt", "object_class": "person"},
{"name": "uav_wakeboard9", "path": "data_seq/UAV123/wakeboard9", "startFrame": 1, "endFrame": 355, "nz": 6,
"ext": "jpg", "anno_path": "anno/UAV123/wakeboard9.txt", "object_class": "person"}
]
return sequence_info_list
\ No newline at end of file
lib/test/evaluation/votdataset.py 0 → 100644
View file @ 75d93027
from typing import Union, TextIO
import numpy as np
from numba import jit
from lib.test.evaluation.data import SequenceList, BaseDataset, Sequence
class VOTDataset(BaseDataset):
"""
VOT2018 dataset
Publication:
The sixth Visual Object Tracking VOT2018 challenge results.
Matej Kristan, Ales Leonardis, Jiri Matas, Michael Felsberg, Roman Pfugfelder, Luka Cehovin Zajc, Tomas Vojir,
Goutam Bhat, Alan Lukezic et al.
ECCV, 2018
https://prints.vicos.si/publications/365
Download the dataset from http://www.votchallenge.net/vot2018/dataset.html
"""
def __init__(self, year=18):
super().__init__()
self.year = year
if year == 18:
self.base_path = self.env_settings.vot18_path
elif year == 20:
self.base_path = self.env_settings.vot20_path
elif year == 22:
self.base_path = self.env_settings.vot22_path
self.sequence_list = self._get_sequence_list(year)
def get_sequence_list(self):
return SequenceList([self._construct_sequence(s) for s in self.sequence_list])
def _construct_sequence(self, sequence_name):
sequence_path = sequence_name
nz = 8
ext = 'jpg'
start_frame = 1
anno_path = '{}/{}/groundtruth.txt'.format(self.base_path, sequence_name)
if self.year == 18 or self.year == 22:
try:
ground_truth_rect = np.loadtxt(str(anno_path), dtype=np.float64)
except:
ground_truth_rect = np.loadtxt(str(anno_path), delimiter=',', dtype=np.float64)
end_frame = ground_truth_rect.shape[0]
frames = ['{base_path}/{sequence_path}/color/{frame:0{nz}}.{ext}'.format(base_path=self.base_path,
sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext)
for frame_num in range(start_frame, end_frame+1)]
# Convert gt
if ground_truth_rect.shape[1] > 4:
gt_x_all = ground_truth_rect[:, [0, 2, 4, 6]]
gt_y_all = ground_truth_rect[:, [1, 3, 5, 7]]
x1 = np.amin(gt_x_all, 1).reshape(-1,1)
y1 = np.amin(gt_y_all, 1).reshape(-1,1)
x2 = np.amax(gt_x_all, 1).reshape(-1,1)
y2 = np.amax(gt_y_all, 1).reshape(-1,1)
ground_truth_rect = np.concatenate((x1, y1, x2-x1, y2-y1), 1)
elif self.year == 20:
ground_truth_rect = read_file(str(anno_path))
ground_truth_rect = np.array(ground_truth_rect, dtype=np.float64)
end_frame = ground_truth_rect.shape[0]
frames = ['{base_path}/{sequence_path}/color/{frame:0{nz}}.{ext}'.format(base_path=self.base_path,
sequence_path=sequence_path,
frame=frame_num, nz=nz, ext=ext)
for frame_num in range(start_frame, end_frame + 1)]
else:
raise NotImplementedError
return Sequence(sequence_name, frames, 'vot', ground_truth_rect)
def __len__(self):
return len(self.sequence_list)
def _get_sequence_list(self, year):
if year == 18:
sequence_list= ['ants1',
'ants3',
'bag',
'ball1',
'ball2',
'basketball',
'birds1',
'blanket',
'bmx',
'bolt1',
'bolt2',
'book',
'butterfly',
'car1',
'conduction1',
'crabs1',
'crossing',
'dinosaur',
'drone_across',
'drone_flip',
'drone1',
'fernando',
'fish1',
'fish2',
'fish3',
'flamingo1',
'frisbee',
'girl',
'glove',
'godfather',
'graduate',
'gymnastics1',
'gymnastics2',
'gymnastics3',
'hand',
'handball1',
'handball2',
'helicopter',
'iceskater1',
'iceskater2',
'leaves',
'matrix',
'motocross1',
'motocross2',
'nature',
'pedestrian1',
'rabbit',
'racing',
'road',
'shaking',
'sheep',
'singer2',
'singer3',
'soccer1',
'soccer2',
'soldier',
'tiger',
'traffic',
'wiper',
'zebrafish1']
elif year == 20:
sequence_list= ['agility',
'ants1',
'ball2',
'ball3',
'basketball',
'birds1',
'bolt1',
'book',
'butterfly',
'car1',
'conduction1',
'crabs1',
'dinosaur',
'dribble',
'drone1',
'drone_across',
'drone_flip',
'fernando',
'fish1',
'fish2',
'flamingo1',
'frisbee',
'girl',
'glove',
'godfather',
'graduate',
'gymnastics1',
'gymnastics2',
'gymnastics3',
'hand',
'hand02',
'hand2',
'handball1',
'handball2',
'helicopter',
'iceskater1',
'iceskater2',
'lamb',
'leaves',
'marathon',
'matrix',
'monkey',
'motocross1',
'nature',
'polo',
'rabbit',
'rabbit2',
'road',
'rowing',
'shaking',
'singer2',
'singer3',
'soccer1',
'soccer2',
'soldier',
'surfing',
'tiger',
'wheel',
'wiper',
'zebrafish1']
elif year == 22:
sequence_list= ['agility',
'animal',
'ants1',
'bag',
'ball2',
'ball3',
'basketball',
'birds1',
'birds2',
'bolt1',
'book',
'bubble',
'butterfly',
'car1',
'conduction1',
'crabs1',
'dinosaur',
'diver',
'drone1',
'drone_across',
'fernando',
'fish1',
'fish2',
'flamingo1',
'frisbee',
'girl',
'graduate',
'gymnastics1',
'gymnastics2',
'gymnastics3',
'hand',
'hand2',
'handball1',
'handball2',
'helicopter',
'iceskater1',
'iceskater2',
'kangaroo',
'lamb',
'leaves',
'marathon',
'matrix',
'monkey',
'motocross1',
'nature',
'polo',
'rabbit',
'rabbit2',
'rowing',
'shaking',
'singer2',
'singer3',
'snake',
'soccer1',
'soccer2',
'soldier',
'surfing',
'tennis',
'tiger',
'wheel',
'wiper',
'zebrafish1']
else:
raise NotImplementedError
return sequence_list
def parse(string):
"""
parse string to the appropriate region format and return region object
"""
from vot.region.shapes import Rectangle, Polygon, Mask
if string[0] == 'm':
# input is a mask - decode it
m_, offset_, region = create_mask_from_string(string[1:].split(','))
# return Mask(m_, offset=offset_)
return region
else:
# input is not a mask - check if special, rectangle or polygon
raise NotImplementedError
print('Unknown region format.')
return None
def read_file(fp: Union[str, TextIO]):
if isinstance(fp, str):
with open(fp) as file:
lines = file.readlines()
else:
lines = fp.readlines()
regions = []
# iterate over all lines in the file
for i, line in enumerate(lines):
regions.append(parse(line.strip()))
return regions
def create_mask_from_string(mask_encoding):
"""
mask_encoding: a string in the following format: x0, y0, w, h, RLE
output: mask, offset
mask: 2-D binary mask, size defined in the mask encoding
offset: (x, y) offset of the mask in the image coordinates
"""
elements = [int(el) for el in mask_encoding]
tl_x, tl_y, region_w, region_h = elements[:4]
rle = np.array([el for el in elements[4:]], dtype=np.int32)
# create mask from RLE within target region
mask = rle_to_mask(rle, region_w, region_h)
region = [tl_x, tl_y, region_w, region_h]
return mask, (tl_x, tl_y), region
@jit(nopython=True)
def rle_to_mask(rle, width, height):
"""
rle: input rle mask encoding
each evenly-indexed element represents number of consecutive 0s
each oddly indexed element represents number of consecutive 1s
width and height are dimensions of the mask
output: 2-D binary mask
"""
# allocate list of zeros
v = [0] * (width * height)
# set id of the last different element to the beginning of the vector
idx_ = 0
for i in range(len(rle)):
if i % 2 != 0:
# write as many 1s as RLE says (zeros are already in the vector)
for j in range(rle[i]):
v[idx_+j] = 1
idx_ += rle[i]
\ No newline at end of file
lib/test/parameter/artrack.py 0 → 100644
View file @ 75d93027
from lib.test.utils import TrackerParams
import os
from lib.test.evaluation.environment import env_settings
from lib.config.artrack.config import cfg, update_config_from_file
def parameters(yaml_name: str):
params = TrackerParams()
prj_dir = env_settings().prj_dir
save_dir = env_settings().save_dir
# update default config from yaml file
yaml_file = os.path.join(prj_dir, 'experiments/artrack/%s.yaml' % yaml_name)
update_config_from_file(yaml_file)
params.cfg = cfg
print("test config: ", cfg)
# template and search region
params.template_factor = cfg.TEST.TEMPLATE_FACTOR
params.template_size = cfg.TEST.TEMPLATE_SIZE
params.search_factor = cfg.TEST.SEARCH_FACTOR
params.search_size = cfg.TEST.SEARCH_SIZE
# Network checkpoint path
params.checkpoint = os.path.join(save_dir, "checkpoints/train/artrack/%s/ARTrack_ep%04d.pth.tar" %
(yaml_name, cfg.TEST.EPOCH))
# whether to save boxes from all queries
params.save_all_boxes = False
return params
lib/test/parameter/artrack_seq.py 0 → 100644
View file @ 75d93027
from lib.test.utils import TrackerParams
import os
from lib.test.evaluation.environment import env_settings
from lib.config.artrack_seq.config import cfg, update_config_from_file
def parameters(yaml_name: str):
params = TrackerParams()
prj_dir = env_settings().prj_dir
save_dir = env_settings().save_dir
# update default config from yaml file
yaml_file = os.path.join(prj_dir, 'experiments/artrack_seq/%s.yaml' % yaml_name)
update_config_from_file(yaml_file)
params.cfg = cfg
print("test config: ", cfg)
# template and search region
params.template_factor = cfg.TEST.TEMPLATE_FACTOR
params.template_size = cfg.TEST.TEMPLATE_SIZE
params.search_factor = cfg.TEST.SEARCH_FACTOR
params.search_size = cfg.TEST.SEARCH_SIZE
# Network checkpoint path
params.checkpoint = os.path.join(save_dir, "checkpoints/train/artrack_seq/%s/ARTrackSeq_ep%04d.pth.tar" %
(yaml_name, cfg.TEST.EPOCH))
# whether to save boxes from all queries
params.save_all_boxes = False
return params
lib/test/tracker/artrack.py 0 → 100644
View file @ 75d93027
import math
from lib.models.artrack import build_artrack
from lib.test.tracker.basetracker import BaseTracker
import torch
from lib.test.tracker.vis_utils import gen_visualization
from lib.test.utils.hann import hann2d
from lib.train.data.processing_utils import sample_target
# for debug
import cv2
import os
from lib.test.tracker.data_utils import Preprocessor
from lib.utils.box_ops import clip_box
from lib.utils.ce_utils import generate_mask_cond
import random
class RandomErasing(object):
def __init__(self, EPSILON=0.5, sl=0.02, sh=0.33, r1=0.3, mean=[0.4914, 0.4822, 0.4465]):
self.EPSILON = EPSILON
self.mean = mean
self.sl = sl
self.sh = sh
self.r1 = r1
def __call__(self, img):
if random.uniform(0, 1) > self.EPSILON:
return img
for attempt in range(100):
print(img.size())
area = img.size()[1] * img.size()[2]
target_area = random.uniform(self.sl, self.sh) * area
aspect_ratio = random.uniform(self.r1, 1 / self.r1)
h = int(round(math.sqrt(target_area * aspect_ratio)))
w = int(round(math.sqrt(target_area / aspect_ratio)))
if w < img.size()[2] and h < img.size()[1]:
x1 = random.randint(0, img.size()[1] - h)
y1 = random.randint(0, img.size()[2] - w)
if img.size()[0] == 3:
# img[0, x1:x1+h, y1:y1+w] = random.uniform(0, 1)
# img[1, x1:x1+h, y1:y1+w] = random.uniform(0, 1)
# img[2, x1:x1+h, y1:y1+w] = random.uniform(0, 1)
img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
img[1, x1:x1 + h, y1:y1 + w] = self.mean[1]
img[2, x1:x1 + h, y1:y1 + w] = self.mean[2]
# img[:, x1:x1+h, y1:y1+w] = torch.from_numpy(np.random.rand(3, h, w))
else:
img[0, x1:x1 + h, y1:y1 + w] = self.mean[1]
# img[0, x1:x1+h, y1:y1+w] = torch.from_numpy(np.random.rand(1, h, w))
return img
return img
class ARTrack(BaseTracker):
def __init__(self, params, dataset_name):
super(ARTrack, self).__init__(params)
network = build_artrack(params.cfg, training=False)
print(self.params.checkpoint)
network.load_state_dict(torch.load(self.params.checkpoint, map_location='cpu')['net'], strict=True)
self.cfg = params.cfg
self.bins = self.cfg.MODEL.BINS
self.network = network.cuda()
self.network.eval()
self.preprocessor = Preprocessor()
self.state = None
self.range = self.cfg.MODEL.RANGE
self.feat_sz = self.cfg.TEST.SEARCH_SIZE // self.cfg.MODEL.BACKBONE.STRIDE
# motion constrain
self.output_window = hann2d(torch.tensor([self.feat_sz, self.feat_sz]).long(), centered=True).cuda()
# for debug
self.debug = params.debug
self.use_visdom = params.debug
self.frame_id = 0
self.erase = RandomErasing()
if self.debug:
if not self.use_visdom:
self.save_dir = "debug"
if not os.path.exists(self.save_dir):
os.makedirs(self.save_dir)
else:
# self.add_hook()
self._init_visdom(None, 1)
# for save boxes from all queries
self.save_all_boxes = params.save_all_boxes
self.z_dict1 = {}
def initialize(self, image, info: dict):
# forward the template once
z_patch_arr, resize_factor, z_amask_arr = sample_target(image, info['init_bbox'], self.params.template_factor,
output_sz=self.params.template_size)#output_sz=self.params.template_size
self.z_patch_arr = z_patch_arr
template = self.preprocessor.process(z_patch_arr, z_amask_arr)
with torch.no_grad():
self.z_dict1 = template
self.box_mask_z = None
#if self.cfg.MODEL.BACKBONE.CE_LOC:
# template_bbox = self.transform_bbox_to_crop(info['init_bbox'], resize_factor,
# template.tensors.device).squeeze(1)
# self.box_mask_z = generate_mask_cond(self.cfg, 1, template.tensors.device, template_bbox)
# save states
self.state = info['init_bbox']
self.frame_id = 0
if self.save_all_boxes:
'''save all predicted boxes'''
all_boxes_save = info['init_bbox'] * self.cfg.MODEL.NUM_OBJECT_QUERIES
return {"all_boxes": all_boxes_save}
def track(self, image, info: dict = None):
magic_num = (self.range - 1) * 0.5
H, W, _ = image.shape
self.frame_id += 1
x_patch_arr, resize_factor, x_amask_arr = sample_target(image, self.state, self.params.search_factor,
output_sz=self.params.search_size) # (x1, y1, w, h)
search = self.preprocessor.process(x_patch_arr, x_amask_arr)
with torch.no_grad():
x_dict = search
# merge the template and the search
# run the transformer
out_dict = self.network.forward(
template=self.z_dict1.tensors, search=x_dict.tensors)
# add hann windows
# pred_score_map = out_dict['score_map']
# response = self.output_window * pred_score_map
# pred_boxes = self.network.box_head.cal_bbox(response, out_dict['size_map'], out_dict['offset_map'])
# pred_boxes = pred_boxes.view(-1, 4)
pred_boxes = out_dict['seqs'][:, 0:4] / (self.bins - 1) - magic_num
pred_boxes = pred_boxes.view(-1, 4).mean(dim=0)
pred_new = pred_boxes
pred_new[2] = pred_boxes[2] - pred_boxes[0]
pred_new[3] = pred_boxes[3] - pred_boxes[1]
pred_new[0] = pred_boxes[0] + pred_boxes[2]/2
pred_new[1] = pred_boxes[1] + pred_boxes[3]/2
pred_boxes = (pred_new * self.params.search_size / resize_factor).tolist()
# Baseline: Take the mean of all pred boxes as the final result
#pred_box = (pred_boxes.mean(
# dim=0) * self.params.search_size / resize_factor).tolist() # (cx, cy, w, h) [0,1]
# get the final box result
self.state = clip_box(self.map_box_back(pred_boxes, resize_factor), H, W, margin=10)
# for debug
if self.debug:
if not self.use_visdom:
x1, y1, w, h = self.state
image_BGR = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
cv2.rectangle(image_BGR, (int(x1),int(y1)), (int(x1+w),int(y1+h)), color=(0,0,255), thickness=2)
save_path = os.path.join(self.save_dir, "%04d.jpg" % self.frame_id)
cv2.imwrite(save_path, image_BGR)
else:
self.visdom.register((image, info['gt_bbox'].tolist(), self.state), 'Tracking', 1, 'Tracking')
self.visdom.register(torch.from_numpy(x_patch_arr).permute(2, 0, 1), 'image', 1, 'search_region')
self.visdom.register(torch.from_numpy(self.z_patch_arr).permute(2, 0, 1), 'image', 1, 'template')
self.visdom.register(pred_score_map.view(self.feat_sz, self.feat_sz), 'heatmap', 1, 'score_map')
self.visdom.register((pred_score_map * self.output_window).view(self.feat_sz, self.feat_sz), 'heatmap', 1, 'score_map_hann')
if 'removed_indexes_s' in out_dict and out_dict['removed_indexes_s']:
removed_indexes_s = out_dict['removed_indexes_s']
removed_indexes_s = [removed_indexes_s_i.cpu().numpy() for removed_indexes_s_i in removed_indexes_s]
masked_search = gen_visualization(x_patch_arr, removed_indexes_s)
self.visdom.register(torch.from_numpy(masked_search).permute(2, 0, 1), 'image', 1, 'masked_search')
while self.pause_mode:
if self.step:
self.step = False
break
if self.save_all_boxes:
'''save all predictions'''
all_boxes = self.map_box_back_batch(pred_boxes * self.params.search_size / resize_factor, resize_factor)
all_boxes_save = all_boxes.view(-1).tolist() # (4N, )
return {"target_bbox": self.state,
"all_boxes": all_boxes_save}
else:
return {"target_bbox": self.state}
def map_box_back(self, pred_box: list, resize_factor: float):
cx_prev, cy_prev = self.state[0] + 0.5 * self.state[2], self.state[1] + 0.5 * self.state[3]
cx, cy, w, h = pred_box
half_side = 0.5 * self.params.search_size / resize_factor
cx_real = cx + (cx_prev - half_side)
cy_real = cy + (cy_prev - half_side)
#cx_real = cx + cx_prev
#cy_real = cy + cy_prev
return [cx_real - 0.5 * w, cy_real - 0.5 * h, w, h]
def map_box_back_batch(self, pred_box: torch.Tensor, resize_factor: float):
cx_prev, cy_prev = self.state[0] + 0.5 * self.state[2], self.state[1] + 0.5 * self.state[3]
cx, cy, w, h = pred_box.unbind(-1) # (N,4) --> (N,)
half_side = 0.5 * self.params.search_size / resize_factor
cx_real = cx + (cx_prev - half_side)
cy_real = cy + (cy_prev - half_side)
return torch.stack([cx_real - 0.5 * w, cy_real - 0.5 * h, w, h], dim=-1)
def add_hook(self):
conv_features, enc_attn_weights, dec_attn_weights = [], [], []
for i in range(12):
self.network.backbone.blocks[i].attn.register_forward_hook(
# lambda self, input, output: enc_attn_weights.append(output[1])
lambda self, input, output: enc_attn_weights.append(output[1])
)
self.enc_attn_weights = enc_attn_weights
def get_tracker_class():
return ARTrack
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